def __init__(self, train_X=None, valid_X=None, test_X=None, args=None, logger=None):
# Output logger
self.logger = logger
self.outdir = args.get("output_path", defaults["output_path"])
if self.outdir[-1] != '/':
self.outdir = self.outdir+'/'
# Input data - make sure it is a list of shared datasets
self.train_X = raise_data_to_list(train_X)
self.valid_X = raise_data_to_list(valid_X)
self.test_X = raise_data_to_list(test_X)
# variables from the dataset that are used for initialization and image reconstruction
if train_X is None:
self.N_input = args.get("input_size")
if args.get("input_size") is None:
raise AssertionError("Please either specify input_size in the arguments or provide an example train_X for input dimensionality.")
else:
self.N_input = train_X[0].eval().shape[1]
self.root_N_input = numpy.sqrt(self.N_input)
self.is_image = args.get('is_image', defaults['is_image'])
if self.is_image:
self.image_width = args.get('width', self.root_N_input)
self.image_height = args.get('height', self.root_N_input)
#######################################
# Network and training specifications #
#######################################
self.layers = args.get('layers', defaults['layers']) # number hidden layers
self.walkbacks = args.get('walkbacks', defaults['walkbacks']) # number of walkbacks
self.learning_rate = theano.shared(cast32(args.get('learning_rate', defaults['learning_rate']))) # learning rate
self.init_learn_rate = cast32(args.get('learning_rate', defaults['learning_rate']))
self.momentum = theano.shared(cast32(args.get('momentum', defaults['momentum']))) # momentum term
self.annealing = cast32(args.get('annealing', defaults['annealing'])) # exponential annealing coefficient
self.noise_annealing = cast32(args.get('noise_annealing', defaults['noise_annealing'])) # exponential noise annealing coefficient
self.batch_size = args.get('batch_size', defaults['batch_size'])
self.n_epoch = args.get('n_epoch', defaults['n_epoch'])
self.early_stop_threshold = args.get('early_stop_threshold', defaults['early_stop_threshold'])
self.early_stop_length = args.get('early_stop_length', defaults['early_stop_length'])
self.save_frequency = args.get('save_frequency', defaults['save_frequency'])
self.noiseless_h1 = args.get('noiseless_h1', defaults["noiseless_h1"])
self.hidden_add_noise_sigma = theano.shared(cast32(args.get('hidden_add_noise_sigma', defaults["hidden_add_noise_sigma"])))
self.input_salt_and_pepper = theano.shared(cast32(args.get('input_salt_and_pepper', defaults["input_salt_and_pepper"])))
self.input_sampling = args.get('input_sampling', defaults["input_sampling"])
self.vis_init = args.get('vis_init', defaults['vis_init'])
self.layer_sizes = [self.N_input] + [args.get('hidden_size', defaults['hidden_size'])] * self.layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.f_recon = None
self.f_noise = None
# Activation functions!
if args.get('hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for hiddens')
self.hidden_activation = args.get('hidden_activation')
elif args.get('hidden_act') is not None:
self.hidden_activation = get_activation_function(args.get('hidden_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for hiddens'.format(args.get('hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for hiddens")
self.hidden_activation = defaults['hidden_activation']
# Visible layer activation
if args.get('visible_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for visible layer')
self.visible_activation = args.get('visible_activation')
elif args.get('visible_act') is not None:
self.visible_activation = get_activation_function(args.get('visible_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for visible layer'.format(args.get('visible_act')))
else:
log.maybeLog(self.logger, 'Using default activation for visible layer')
self.visible_activation = defaults['visible_activation']
# Cost function!
if args.get('cost_function') is not None:
log.maybeLog(self.logger, '\nUsing specified cost function for training\n')
self.cost_function = args.get('cost_function')
elif args.get('cost_funct') is not None:
self.cost_function = get_cost_function(args.get('cost_funct'))
log.maybeLog(self.logger, 'Using {0!s} for cost function'.format(args.get('cost_funct')))
else:
log.maybeLog(self.logger, '\nUsing default cost function for training\n')
self.cost_function = defaults['cost_function']
############################
# Theano variables and RNG #
############################
self.X = T.fmatrix('X') # for use in sampling
self.MRG = RNG_MRG.MRG_RandomStreams(1)
rng.seed(1)
###############
# Parameters! #
###############
# initialize a list of weights and biases based on layer_sizes for the GSN
if args.get('weights_list') is None:
self.weights_list = [get_shared_weights(self.layer_sizes[layer], self.layer_sizes[layer+1], name="W_{0!s}_{1!s}".format(layer,layer+1)) for layer in range(self.layers)] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
else:
self.weights_list = args.get('weights_list')
if args.get('bias_list') is None:
self.bias_list = [get_shared_bias(self.layer_sizes[layer], name='b_'+str(layer)) for layer in range(self.layers + 1)] # initialize each layer to 0's.
else:
self.bias_list = args.get('bias_list')
self.params = self.weights_list + self.bias_list
#################
# Build the GSN #
#################
log.maybeLog(self.logger, "\nBuilding GSN graphs for training and testing")
# GSN for training - with noise
add_noise = True
p_X_chain, _ = build_gsn(self.X,
self.weights_list,
self.bias_list,
add_noise,
self.noiseless_h1,
self.hidden_add_noise_sigma,
self.input_salt_and_pepper,
self.input_sampling,
self.MRG,
self.visible_activation,
self.hidden_activation,
self.walkbacks,
self.logger)
# GSN for reconstruction checks along the way - no noise
add_noise = False
p_X_chain_recon, _ = build_gsn(self.X,
self.weights_list,
self.bias_list,
add_noise,
self.noiseless_h1,
self.hidden_add_noise_sigma,
self.input_salt_and_pepper,
self.input_sampling,
self.MRG,
self.visible_activation,
self.hidden_activation,
self.walkbacks,
self.logger)
#######################
# Costs and gradients #
#######################
log.maybeLog(self.logger, 'Cost w.r.t p(X|...) at every step in the graph for the GSN')
gsn_costs = [self.cost_function(rX, self.X) for rX in p_X_chain]
show_gsn_cost = gsn_costs[-1] # for logging to show progress
gsn_cost = numpy.sum(gsn_costs)
gsn_costs_recon = [self.cost_function(rX, self.X) for rX in p_X_chain_recon]
show_gsn_cost_recon = gsn_costs_recon[-1]
log.maybeLog(self.logger, ["gsn params:", self.params])
# Stochastic gradient descent!
gradient = T.grad(gsn_cost, self.params)
gradient_buffer = [theano.shared(numpy.zeros(param.get_value().shape, dtype='float32')) for param in self.params]
m_gradient = [self.momentum * gb + (cast32(1) - self.momentum) * g for (gb, g) in zip(gradient_buffer, gradient)]
param_updates = [(param, param - self.learning_rate * mg) for (param, mg) in zip(self.params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
############
# Sampling #
############
# the input to the sampling function
X_sample = T.fmatrix("X_sampling")
self.network_state_input = [X_sample] + [T.fmatrix("H_sampling_"+str(i+1)) for i in range(self.layers)]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
self.network_state_output = [X_sample] + self.network_state_input[1:]
visible_pX_chain = []
# ONE update
log.maybeLog(self.logger, "Performing one walkback in network state sampling.")
update_layers(self.network_state_output,
self.weights_list,
self.bias_list,
visible_pX_chain,
True,
self.noiseless_h1,
self.hidden_add_noise_sigma,
self.input_salt_and_pepper,
self.input_sampling,
self.MRG,
self.visible_activation,
self.hidden_activation,
self.logger)
#################################
# Create the functions #
#################################
log.maybeLog(self.logger, "Compiling functions...")
t = time.time()
self.f_learn = theano.function(inputs = [self.X],
updates = updates,
outputs = show_gsn_cost,
name='gsn_f_learn')
self.f_cost = theano.function(inputs = [self.X],
outputs = show_gsn_cost,
name='gsn_f_cost')
# used for checkpoints and testing - no noise in network
self.f_recon = theano.function(inputs = [self.X],
outputs = [show_gsn_cost_recon, p_X_chain_recon[-1]],
name='gsn_f_recon')
self.f_noise = theano.function(inputs = [self.X],
outputs = salt_and_pepper(self.X, self.input_salt_and_pepper),
name='gsn_f_noise')
if self.layers == 1:
self.f_sample = theano.function(inputs = [X_sample],
outputs = visible_pX_chain[-1],
name='gsn_f_sample_single_layer')
else:
# WHY IS THERE A WARNING????
# because the first odd layers are not used -> directly computed FROM THE EVEN layers
# unused input = warn
self.f_sample = theano.function(inputs = self.network_state_input,
outputs = self.network_state_output + visible_pX_chain,
on_unused_input='warn',
name='gsn_f_sample')
log.maybeLog(self.logger, "Compiling done. Took "+make_time_units_string(time.time() - t)+".\n")
def __init__(self, train_X=None, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None, args=None, logger=None):
# Output logger
self.logger = logger
self.outdir = args.get("output_path", defaults["output_path"])
if self.outdir[-1] != '/':
self.outdir = self.outdir+'/'
# Input data
self.train_X = train_X
self.train_Y = train_Y
self.valid_X = valid_X
self.valid_Y = valid_Y
self.test_X = test_X
self.test_Y = test_Y
# variables from the dataset that are used for initialization and image reconstruction
if train_X is None:
self.N_input = args.get("input_size")
if args.get("input_size") is None:
raise AssertionError("Please either specify input_size in the arguments or provide an example train_X for input dimensionality.")
else:
self.N_input = train_X.eval().shape[1]
self.root_N_input = numpy.sqrt(self.N_input)
self.is_image = args.get('is_image', defaults['is_image'])
if self.is_image:
self.image_width = args.get('width', self.root_N_input)
self.image_height = args.get('height', self.root_N_input)
#######################################
# Network and training specifications #
#######################################
self.gsn_layers = args.get('gsn_layers', defaults['gsn_layers']) # number hidden layers
self.walkbacks = args.get('walkbacks', defaults['walkbacks']) # number of walkbacks
self.learning_rate = theano.shared(cast32(args.get('learning_rate', defaults['learning_rate']))) # learning rate
self.init_learn_rate = cast32(args.get('learning_rate', defaults['learning_rate']))
self.momentum = theano.shared(cast32(args.get('momentum', defaults['momentum']))) # momentum term
self.annealing = cast32(args.get('annealing', defaults['annealing'])) # exponential annealing coefficient
self.noise_annealing = cast32(args.get('noise_annealing', defaults['noise_annealing'])) # exponential noise annealing coefficient
self.batch_size = args.get('batch_size', defaults['batch_size'])
self.gsn_batch_size = args.get('gsn_batch_size', defaults['gsn_batch_size'])
self.n_epoch = args.get('n_epoch', defaults['n_epoch'])
self.early_stop_threshold = args.get('early_stop_threshold', defaults['early_stop_threshold'])
self.early_stop_length = args.get('early_stop_length', defaults['early_stop_length'])
self.save_frequency = args.get('save_frequency', defaults['save_frequency'])
self.noiseless_h1 = args.get('noiseless_h1', defaults["noiseless_h1"])
self.hidden_add_noise_sigma = theano.shared(cast32(args.get('hidden_add_noise_sigma', defaults["hidden_add_noise_sigma"])))
self.input_salt_and_pepper = theano.shared(cast32(args.get('input_salt_and_pepper', defaults["input_salt_and_pepper"])))
self.input_sampling = args.get('input_sampling', defaults["input_sampling"])
self.vis_init = args.get('vis_init', defaults['vis_init'])
self.load_params = args.get('load_params', defaults['load_params'])
self.hessian_free = args.get('hessian_free', defaults['hessian_free'])
self.layer_sizes = [self.N_input] + [args.get('hidden_size', defaults['hidden_size'])] * self.gsn_layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.recurrent_hidden_size = args.get('recurrent_hidden_size', defaults['recurrent_hidden_size'])
self.top_layer_sizes = [self.recurrent_hidden_size] + [args.get('hidden_size', defaults['hidden_size'])] * self.gsn_layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.f_recon = None
self.f_noise = None
# Activation functions!
# For the GSN:
if args.get('hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for GSN hiddens')
self.hidden_activation = args.get('hidden_activation')
elif args.get('hidden_act') == 'sigmoid':
log.maybeLog(self.logger, 'Using sigmoid activation for GSN hiddens')
self.hidden_activation = T.nnet.sigmoid
elif args.get('hidden_act') == 'rectifier':
log.maybeLog(self.logger, 'Using rectifier activation for GSN hiddens')
self.hidden_activation = lambda x : T.maximum(cast32(0), x)
elif args.get('hidden_act') == 'tanh':
log.maybeLog(self.logger, 'Using hyperbolic tangent activation for GSN hiddens')
self.hidden_activation = lambda x : T.tanh(x)
elif args.get('hidden_act') is not None:
log.maybeLog(self.logger, "Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for GSN hiddens".format(args.get('hidden_act')))
raise NotImplementedError("Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for GSN hiddens".format(args.get('hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for GSN hiddens")
self.hidden_activation = defaults['hidden_activation']
# For the RNN:
if args.get('recurrent_hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for RNN hiddens')
self.recurrent_hidden_activation = args.get('recurrent_hidden_activation')
elif args.get('recurrent_hidden_act') == 'sigmoid':
log.maybeLog(self.logger, 'Using sigmoid activation for RNN hiddens')
self.recurrent_hidden_activation = T.nnet.sigmoid
elif args.get('recurrent_hidden_act') == 'rectifier':
log.maybeLog(self.logger, 'Using rectifier activation for RNN hiddens')
self.recurrent_hidden_activation = lambda x : T.maximum(cast32(0), x)
elif args.get('recurrent_hidden_act') == 'tanh':
log.maybeLog(self.logger, 'Using hyperbolic tangent activation for RNN hiddens')
self.recurrent_hidden_activation = lambda x : T.tanh(x)
elif args.get('recurrent_hidden_act') is not None:
log.maybeLog(self.logger, "Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for RNN hiddens".format(args.get('hidden_act')))
raise NotImplementedError("Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for RNN hiddens".format(args.get('hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for RNN hiddens")
self.recurrent_hidden_activation = defaults['recurrent_hidden_activation']
# Visible layer activation
if args.get('visible_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for visible layer')
self.visible_activation = args.get('visible_activation')
elif args.get('visible_act') == 'sigmoid':
log.maybeLog(self.logger, 'Using sigmoid activation for visible layer')
self.visible_activation = T.nnet.sigmoid
elif args.get('visible_act') == 'softmax':
log.maybeLog(self.logger, 'Using softmax activation for visible layer')
self.visible_activation = T.nnet.softmax
elif args.get('visible_act') is not None:
log.maybeLog(self.logger, "Did not recognize visible activation {0!s}, please use sigmoid or softmax".format(args.get('visible_act')))
raise NotImplementedError("Did not recognize visible activation {0!s}, please use sigmoid or softmax".format(args.get('visible_act')))
else:
log.maybeLog(self.logger, 'Using default activation for visible layer')
self.visible_activation = defaults['visible_activation']
# Cost function!
if args.get('cost_function') is not None:
log.maybeLog(self.logger, '\nUsing specified cost function for GSN training\n')
self.cost_function = args.get('cost_function')
elif args.get('cost_funct') == 'binary_crossentropy':
log.maybeLog(self.logger, '\nUsing binary cross-entropy cost!\n')
self.cost_function = lambda x,y: T.mean(T.nnet.binary_crossentropy(x,y))
elif args.get('cost_funct') == 'square':
log.maybeLog(self.logger, "\nUsing square error cost!\n")
#cost_function = lambda x,y: T.log(T.mean(T.sqr(x-y)))
self.cost_function = lambda x,y: T.log(T.sum(T.pow((x-y),2)))
elif args.get('cost_funct') is not None:
log.maybeLog(self.logger, "\nDid not recognize cost function {0!s}, please use binary_crossentropy or square\n".format(args.get('cost_funct')))
raise NotImplementedError("Did not recognize cost function {0!s}, please use binary_crossentropy or square".format(args.get('cost_funct')))
else:
log.maybeLog(self.logger, '\nUsing default cost function for GSN training\n')
self.cost_function = defaults['cost_function']
############################
# Theano variables and RNG #
############################
self.X = T.fmatrix('X') #single (batch) for training gsn
self.Xs = T.fmatrix('Xs') #sequence for training rnn
self.MRG = RNG_MRG.MRG_RandomStreams(1)
###############
# Parameters! #
###############
#visible gsn
self.weights_list = [get_shared_weights(self.layer_sizes[i], self.layer_sizes[i+1], name="W_{0!s}_{1!s}".format(i,i+1)) for i in range(self.gsn_layers)] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
self.bias_list = [get_shared_bias(self.layer_sizes[i], name='b_'+str(i)) for i in range(self.gsn_layers + 1)] # initialize each layer to 0's.
#recurrent
self.recurrent_to_gsn_weights_list = [get_shared_weights(self.recurrent_hidden_size, self.layer_sizes[layer], name="W_u_h{0!s}".format(layer)) for layer in range(self.gsn_layers+1) if layer%2 != 0]
self.W_u_u = get_shared_weights(self.recurrent_hidden_size, self.recurrent_hidden_size, name="W_u_u")
self.W_ins_u = get_shared_weights(args.get('hidden_size', defaults['hidden_size']), self.recurrent_hidden_size, name="W_ins_u")
self.recurrent_bias = get_shared_bias(self.recurrent_hidden_size, name='b_u')
#top layer gsn
self.top_weights_list = [get_shared_weights(self.top_layer_sizes[i], self.top_layer_sizes[i+1], name="Wtop_{0!s}_{1!s}".format(i,i+1)) for i in range(self.gsn_layers)] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
self.top_bias_list = [get_shared_bias(self.top_layer_sizes[i], name='btop_'+str(i)) for i in range(self.gsn_layers + 1)] # initialize each layer to 0's.
#lists for use with gradients
self.gsn_params = self.weights_list + self.bias_list
self.u_params = [self.W_u_u, self.W_ins_u, self.recurrent_bias]
self.top_params = self.top_weights_list + self.top_bias_list
self.params = self.gsn_params + self.recurrent_to_gsn_weights_list + self.u_params + self.top_params
###################################################
# load initial parameters #
###################################################
if self.load_params:
params_to_load = 'gsn_params.pkl'
log.maybeLog(self.logger, "\nLoading existing GSN parameters\n")
loaded_params = cPickle.load(open(params_to_load,'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(self.weights_list)], self.weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.weights_list):], self.bias_list)]
params_to_load = 'rnn_params.pkl'
log.maybeLog(self.logger, "\nLoading existing RNN parameters\n")
loaded_params = cPickle.load(open(params_to_load,'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(self.recurrent_to_gsn_weights_list)], self.recurrent_to_gsn_weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.recurrent_to_gsn_weights_list):len(self.recurrent_to_gsn_weights_list)+1], self.W_u_u)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.recurrent_to_gsn_weights_list)+1:len(self.recurrent_to_gsn_weights_list)+2], self.W_ins_u)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.recurrent_to_gsn_weights_list)+2:], self.recurrent_bias)]
params_to_load = 'top_gsn_params.pkl'
log.maybeLog(self.logger, "\nLoading existing top level GSN parameters\n")
loaded_params = cPickle.load(open(params_to_load,'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(self.top_weights_list)], self.top_weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.top_weights_list):], self.top_bias_list)]
self.gsn_args = {'weights_list': self.weights_list,
'bias_list': self.bias_list,
'hidden_activation': self.hidden_activation,
'visible_activation': self.visible_activation,
'cost_function': self.cost_function,
'layers': self.gsn_layers,
'walkbacks': self.walkbacks,
'hidden_size': args.get('hidden_size', defaults['hidden_size']),
'learning_rate': args.get('learning_rate', defaults['learning_rate']),
'momentum': args.get('momentum', defaults['momentum']),
'annealing': self.annealing,
'noise_annealing': self.noise_annealing,
'batch_size': self.gsn_batch_size,
'n_epoch': self.n_epoch,
'early_stop_threshold': self.early_stop_threshold,
'early_stop_length': self.early_stop_length,
'save_frequency': self.save_frequency,
'noiseless_h1': self.noiseless_h1,
'hidden_add_noise_sigma': args.get('hidden_add_noise_sigma', defaults['hidden_add_noise_sigma']),
'input_salt_and_pepper': args.get('input_salt_and_pepper', defaults['input_salt_and_pepper']),
'input_sampling': self.input_sampling,
'vis_init': self.vis_init,
'output_path': self.outdir+'gsn/',
'is_image': self.is_image,
'input_size': self.N_input
}
self.top_gsn_args = {'weights_list': self.top_weights_list,
'bias_list': self.top_bias_list,
'hidden_activation': self.hidden_activation,
'visible_activation': self.recurrent_hidden_activation,
'cost_function': self.cost_function,
'layers': self.gsn_layers,
'walkbacks': self.walkbacks,
'hidden_size': args.get('hidden_size', defaults['hidden_size']),
'learning_rate': args.get('learning_rate', defaults['learning_rate']),
'momentum': args.get('momentum', defaults['momentum']),
'annealing': self.annealing,
'noise_annealing': self.noise_annealing,
'batch_size': self.gsn_batch_size,
'n_epoch': self.n_epoch,
'early_stop_threshold': self.early_stop_threshold,
'early_stop_length': self.early_stop_length,
'save_frequency': self.save_frequency,
'noiseless_h1': self.noiseless_h1,
'hidden_add_noise_sigma': args.get('hidden_add_noise_sigma', defaults['hidden_add_noise_sigma']),
'input_salt_and_pepper': args.get('input_salt_and_pepper', defaults['input_salt_and_pepper']),
'input_sampling': self.input_sampling,
'vis_init': self.vis_init,
'output_path': self.outdir+'top_gsn/',
'is_image': False,
'input_size': self.recurrent_hidden_size
}
############
# Sampling #
############
# the input to the sampling function
X_sample = T.fmatrix("X_sampling")
self.network_state_input = [X_sample] + [T.fmatrix("H_sampling_"+str(i+1)) for i in range(self.gsn_layers)]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
self.network_state_output = [X_sample] + self.network_state_input[1:]
visible_pX_chain = []
# ONE update
log.maybeLog(self.logger, "Performing one walkback in network state sampling.")
generative_stochastic_network.update_layers(self.network_state_output,
self.weights_list,
self.bias_list,
visible_pX_chain,
True,
self.noiseless_h1,
self.hidden_add_noise_sigma,
self.input_salt_and_pepper,
self.input_sampling,
self.MRG,
self.visible_activation,
self.hidden_activation,
self.logger)
##############################################
# Build the graphs for the SEN #
##############################################
# If `x_t` is given, deterministic recurrence to compute the u_t. Otherwise, first generate
def recurrent_step(x_t, u_tm1, add_noise):
# Make current guess for hiddens based on U
for i in range(self.gsn_layers):
if i%2 == 0:
log.maybeLog(self.logger, "Using {0!s} and {1!s}".format(self.recurrent_to_gsn_weights_list[(i+1)/2],self.bias_list[i+1]))
h_t = T.concatenate([self.hidden_activation(self.bias_list[i+1] + T.dot(u_tm1, self.recurrent_to_gsn_weights_list[(i+1)/2])) for i in range(self.gsn_layers) if i%2 == 0],axis=0)
# Make a GSN to update U
_, hs = generative_stochastic_network.build_gsn(x_t, self.weights_list, self.bias_list, add_noise, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.logger)
htop_t = hs[-1]
ins_t = htop_t
ua_t = T.dot(ins_t, self.W_ins_u) + T.dot(u_tm1, self.W_u_u) + self.recurrent_bias
u_t = self.recurrent_hidden_activation(ua_t)
return [ua_t, u_t, h_t]
log.maybeLog(self.logger, "\nCreating recurrent step scan.")
# For training, the deterministic recurrence is used to compute all the
# {h_t, 1 <= t <= T} given Xs. Conditional GSNs can then be trained
# in batches using those parameters.
u0 = T.zeros((self.recurrent_hidden_size,)) # initial value for the RNN hidden units
(ua, u, h_t), updates_recurrent = theano.scan(fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, True),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
log.maybeLog(self.logger, "Now for reconstruction sample without noise")
(_, _, h_t_recon), updates_recurrent_recon = theano.scan(fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, False),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
# put together the hiddens list
h_list = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer%2 != 0:
h_list.append(T.zeros_like(T.dot(h_list[-1], w)))
else:
h_list.append((h_t.T[(layer/2)*self.hidden_size:(layer/2+1)*self.hidden_size]).T)
h_list_recon = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer%2 != 0:
h_list_recon.append(T.zeros_like(T.dot(h_list_recon[-1], w)))
else:
h_list_recon.append((h_t_recon.T[(layer/2)*self.hidden_size:(layer/2+1)*self.hidden_size]).T)
#with noise
_, cost, show_cost = generative_stochastic_network.build_gsn_given_hiddens(self.Xs, h_list, self.weights_list, self.bias_list, True, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.cost_function, self.logger)
#without noise for reconstruction
x_sample_recon, _, _ = generative_stochastic_network.build_gsn_given_hiddens(self.Xs, h_list_recon, self.weights_list, self.bias_list, False, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.cost_function, self.logger)
updates_train = updates_recurrent
updates_cost = updates_recurrent
#############
# COSTS #
#############
log.maybeLog(self.logger, '\nCost w.r.t p(X|...) at every step in the graph')
start_functions_time = time.time()
# if we are not using Hessian-free training create the normal sgd functions
if not self.hessian_free:
gradient = T.grad(cost, self.params)
gradient_buffer = [theano.shared(numpy.zeros(param.get_value().shape, dtype='float32')) for param in self.params]
m_gradient = [self.momentum * gb + (cast32(1) - self.momentum) * g for (gb, g) in zip(gradient_buffer, gradient)]
param_updates = [(param, param - self.learning_rate * mg) for (param, mg) in zip(self.params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
updates_train.update(updates)
log.maybeLog(self.logger, "rnn-gsn learn...")
self.f_learn = theano.function(inputs = [self.Xs],
updates = updates_train,
outputs = show_cost,
on_unused_input='warn',
name='rnngsn_f_learn')
log.maybeLog(self.logger, "rnn-gsn cost...")
self.f_cost = theano.function(inputs = [self.Xs],
updates = updates_cost,
outputs = show_cost,
on_unused_input='warn',
name='rnngsn_f_cost')
log.maybeLog(self.logger, "Training/cost functions done.")
# Denoise some numbers : show number, noisy number, predicted number, reconstructed number
log.maybeLog(self.logger, "Creating graph for noisy reconstruction function at checkpoints during training.")
self.f_recon = theano.function(inputs=[self.Xs],
outputs=x_sample_recon[-1],
updates=updates_recurrent_recon,
name='rnngsn_f_recon')
# a function to add salt and pepper noise
self.f_noise = theano.function(inputs = [self.X],
outputs = salt_and_pepper(self.X, self.input_salt_and_pepper),
name='rnngsn_f_noise')
# Sampling functions
log.maybeLog(self.logger, "Creating sampling function...")
if self.gsn_layers == 1:
self.f_sample = theano.function(inputs = [X_sample],
outputs = visible_pX_chain[-1],
name='rnngsn_f_sample_single_layer')
else:
# WHY IS THERE A WARNING????
# because the first odd layers are not used -> directly computed FROM THE EVEN layers
# unused input = warn
self.f_sample = theano.function(inputs = self.network_state_input,
outputs = self.network_state_output + visible_pX_chain,
on_unused_input='warn',
name='rnngsn_f_sample')
log.maybeLog(self.logger, "Done compiling all functions.")
compilation_time = time.time() - start_functions_time
# Show the compile time with appropriate easy-to-read units.
log.maybeLog(self.logger, "Total compilation time took "+make_time_units_string(compilation_time)+".\n\n")
def __init__(self, train_X=None, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None, args=None, logger=None):
# Output logger
self.logger = logger
self.outdir = args.get("output_path", defaults["output_path"])
if self.outdir[-1] != '/':
self.outdir = self.outdir+'/'
data.mkdir_p(self.outdir)
# Configuration
config_filename = self.outdir+'config'
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(args))
# Input data - make sure it is a list of shared datasets if it isn't. THIS WILL KEEP 'NONE' AS 'NONE' no need to worry :)
self.train_X = raise_to_list(train_X)
self.train_Y = raise_to_list(train_Y)
self.valid_X = raise_to_list(valid_X)
self.valid_Y = raise_to_list(valid_Y)
self.test_X = raise_to_list(test_X)
self.test_Y = raise_to_list(test_Y)
# variables from the dataset that are used for initialization and image reconstruction
if self.train_X is None:
self.N_input = args.get("input_size")
if args.get("input_size") is None:
raise AssertionError("Please either specify input_size in the arguments or provide an example train_X for input dimensionality.")
else:
self.N_input = self.train_X[0].get_value(borrow=True).shape[1]
self.is_image = args.get('is_image', defaults['is_image'])
if self.is_image:
(_h, _w) = closest_to_square_factors(self.N_input)
self.image_width = args.get('width', _w)
self.image_height = args.get('height', _h)
#######################################
# Network and training specifications #
#######################################
self.layers = args.get('layers', defaults['layers']) # number hidden layers
self.walkbacks = args.get('walkbacks', defaults['walkbacks']) # number of walkbacks
self.learning_rate = theano.shared(cast32(args.get('learning_rate', defaults['learning_rate']))) # learning rate
self.init_learn_rate = cast32(args.get('learning_rate', defaults['learning_rate']))
self.momentum = theano.shared(cast32(args.get('momentum', defaults['momentum']))) # momentum term
self.annealing = cast32(args.get('annealing', defaults['annealing'])) # exponential annealing coefficient
self.noise_annealing = cast32(args.get('noise_annealing', defaults['noise_annealing'])) # exponential noise annealing coefficient
self.batch_size = args.get('batch_size', defaults['batch_size'])
self.gsn_batch_size = args.get('gsn_batch_size', defaults['gsn_batch_size'])
self.n_epoch = args.get('n_epoch', defaults['n_epoch'])
self.early_stop_threshold = args.get('early_stop_threshold', defaults['early_stop_threshold'])
self.early_stop_length = args.get('early_stop_length', defaults['early_stop_length'])
self.save_frequency = args.get('save_frequency', defaults['save_frequency'])
self.noiseless_h1 = args.get('noiseless_h1', defaults["noiseless_h1"])
self.hidden_add_noise_sigma = theano.shared(cast32(args.get('hidden_add_noise_sigma', defaults["hidden_add_noise_sigma"])))
self.input_salt_and_pepper = theano.shared(cast32(args.get('input_salt_and_pepper', defaults["input_salt_and_pepper"])))
self.input_sampling = args.get('input_sampling', defaults["input_sampling"])
self.vis_init = args.get('vis_init', defaults['vis_init'])
self.initialize_gsn = args.get('initialize_gsn', defaults['initialize_gsn'])
self.hessian_free = args.get('hessian_free', defaults['hessian_free'])
self.hidden_size = args.get('hidden_size', defaults['hidden_size'])
self.layer_sizes = [self.N_input] + [self.hidden_size] * self.layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.recurrent_hidden_size = args.get('recurrent_hidden_size', defaults['recurrent_hidden_size'])
self.f_recon = None
self.f_noise = None
# Activation functions!
# For the GSN:
if args.get('hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for GSN hiddens')
self.hidden_activation = args.get('hidden_activation')
elif args.get('hidden_act') is not None:
self.hidden_activation = get_activation_function(args.get('hidden_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for GSN hiddens'.format(args.get('hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for GSN hiddens")
self.hidden_activation = defaults['hidden_activation']
# For the RNN:
if args.get('recurrent_hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for RNN hiddens')
self.recurrent_hidden_activation = args.get('recurrent_hidden_activation')
elif args.get('recurrent_hidden_act') is not None:
self.recurrent_hidden_activation = get_activation_function(args.get('recurrent_hidden_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for RNN hiddens'.format(args.get('recurrent_hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for RNN hiddens")
self.recurrent_hidden_activation = defaults['recurrent_hidden_activation']
# Visible layer activation
if args.get('visible_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for visible layer')
self.visible_activation = args.get('visible_activation')
elif args.get('visible_act') is not None:
self.visible_activation = get_activation_function(args.get('visible_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for visible layer'.format(args.get('visible_act')))
else:
log.maybeLog(self.logger, 'Using default activation for visible layer')
self.visible_activation = defaults['visible_activation']
# Cost function!
if args.get('cost_function') is not None:
log.maybeLog(self.logger, '\nUsing specified cost function for GSN training\n')
self.cost_function = args.get('cost_function')
elif args.get('cost_funct') is not None:
self.cost_function = get_cost_function(args.get('cost_funct'))
log.maybeLog(self.logger, 'Using {0!s} for cost function'.format(args.get('cost_funct')))
else:
log.maybeLog(self.logger, '\nUsing default cost function for GSN training\n')
self.cost_function = defaults['cost_function']
############################
# Theano variables and RNG #
############################
self.X = T.fmatrix('X') #single (batch) for training gsn
self.Xs = T.fmatrix('Xs') #sequence for training rnn-gsn
self.MRG = RNG_MRG.MRG_RandomStreams(1)
###############
# Parameters! #
###############
#gsn
self.weights_list = [get_shared_weights(self.layer_sizes[i], self.layer_sizes[i+1], name="W_{0!s}_{1!s}".format(i,i+1)) for i in range(self.layers)] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
self.bias_list = [get_shared_bias(self.layer_sizes[i], name='b_'+str(i)) for i in range(self.layers + 1)] # initialize each layer to 0's.
#recurrent
self.recurrent_to_gsn_weights_list = [get_shared_weights(self.recurrent_hidden_size, self.layer_sizes[layer], name="W_u_h{0!s}".format(layer)) for layer in range(self.layers+1) if layer%2 != 0]
self.W_u_u = get_shared_weights(self.recurrent_hidden_size, self.recurrent_hidden_size, name="W_u_u")
self.W_x_u = get_shared_weights(self.N_input, self.recurrent_hidden_size, name="W_x_u")
self.recurrent_bias = get_shared_bias(self.recurrent_hidden_size, name='b_u')
#lists for use with gradients
self.gsn_params = self.weights_list + self.bias_list
self.u_params = [self.W_u_u, self.W_x_u, self.recurrent_bias]
self.params = self.gsn_params + self.recurrent_to_gsn_weights_list + self.u_params
###########################################################
# load initial parameters of gsn #
###########################################################
self.train_gsn_first = False
if self.initialize_gsn:
params_to_load = 'gsn_params_epoch_30.pkl'
if not os.path.isfile(params_to_load):
self.train_gsn_first = True
else:
log.maybeLog(self.logger, "\nLoading existing GSN parameters\n")
loaded_params = cPickle.load(open(params_to_load,'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(self.weights_list)], self.weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.weights_list):], self.bias_list)]
if self.initialize_gsn:
self.gsn_args = {'weights_list': self.weights_list,
'bias_list': self.bias_list,
'hidden_activation': self.hidden_activation,
'visible_activation': self.visible_activation,
'cost_function': self.cost_function,
'layers': self.layers,
'walkbacks': self.walkbacks,
'hidden_size': self.hidden_size,
'learning_rate': args.get('learning_rate', defaults['learning_rate']),
'momentum': args.get('momentum', defaults['momentum']),
'annealing': self.annealing,
'noise_annealing': self.noise_annealing,
'batch_size': self.gsn_batch_size,
'n_epoch': self.n_epoch,
'early_stop_threshold': self.early_stop_threshold,
'early_stop_length': self.early_stop_length,
'save_frequency': self.save_frequency,
'noiseless_h1': self.noiseless_h1,
'hidden_add_noise_sigma': args.get('hidden_add_noise_sigma', defaults['hidden_add_noise_sigma']),
'input_salt_and_pepper': args.get('input_salt_and_pepper', defaults['input_salt_and_pepper']),
'input_sampling': self.input_sampling,
'vis_init': self.vis_init,
'output_path': self.outdir+'gsn/',
'is_image': self.is_image,
'input_size': self.N_input
}
############
# Sampling #
############
# the input to the sampling function
X_sample = T.fmatrix("X_sampling")
self.network_state_input = [X_sample] + [T.fmatrix("H_sampling_"+str(i+1)) for i in range(self.layers)]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
self.network_state_output = [X_sample] + self.network_state_input[1:]
visible_pX_chain = []
# ONE update
_add_noise = True
log.maybeLog(self.logger, "Performing one walkback in network state sampling.")
GSN.update_layers(self.network_state_output,
self.weights_list,
self.bias_list,
visible_pX_chain,
_add_noise,
self.noiseless_h1,
self.hidden_add_noise_sigma,
self.input_salt_and_pepper,
self.input_sampling,
self.MRG,
self.visible_activation,
self.hidden_activation,
self.logger)
#############################################
# Build the graphs for the RNN-GSN #
#############################################
# If `x_t` is given, deterministic recurrence to compute the u_t. Otherwise, first generate
def recurrent_step(x_t, u_tm1, add_noise):
# Make current guess for hiddens based on U
for i in range(self.layers):
if i%2 == 0:
log.maybeLog(self.logger, "Using {0!s} and {1!s}".format(self.recurrent_to_gsn_weights_list[(i+1)/2],self.bias_list[i+1]))
h_t = T.concatenate([self.hidden_activation(self.bias_list[i+1] + T.dot(u_tm1, self.recurrent_to_gsn_weights_list[(i+1)/2])) for i in range(self.layers) if i%2 == 0],axis=0)
generate = x_t is None
if generate:
pass
# Make a GSN to update U
# chain, hs = gsn.build_gsn(x_t, weights_list, bias_list, add_noise, state.noiseless_h1, state.hidden_add_noise_sigma, state.input_salt_and_pepper, state.input_sampling, MRG, visible_activation, hidden_activation, walkbacks, logger)
# htop_t = hs[-1]
# denoised_x_t = chain[-1]
# Update U
# ua_t = T.dot(denoised_x_t, W_x_u) + T.dot(htop_t, W_h_u) + T.dot(u_tm1, W_u_u) + recurrent_bias
ua_t = T.dot(x_t, self.W_x_u) + T.dot(u_tm1, self.W_u_u) + self.recurrent_bias
u_t = self.recurrent_hidden_activation(ua_t)
return None if generate else [ua_t, u_t, h_t]
log.maybeLog(self.logger, "\nCreating recurrent step scan.")
# For training, the deterministic recurrence is used to compute all the
# {h_t, 1 <= t <= T} given Xs. Conditional GSNs can then be trained
# in batches using those parameters.
u0 = T.zeros((self.recurrent_hidden_size,)) # initial value for the RNN hidden units
(ua, u, h_t), updates_recurrent = theano.scan(fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, True),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
log.maybeLog(self.logger, "Now for reconstruction sample without noise")
(_, _, h_t_recon), updates_recurrent_recon = theano.scan(fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, False),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
# put together the hiddens list
h_list = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer%2 != 0:
h_list.append(T.zeros_like(T.dot(h_list[-1], w)))
else:
h_list.append((h_t.T[(layer/2)*self.hidden_size:(layer/2+1)*self.hidden_size]).T)
h_list_recon = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer%2 != 0:
h_list_recon.append(T.zeros_like(T.dot(h_list_recon[-1], w)))
else:
h_list_recon.append((h_t_recon.T[(layer/2)*self.hidden_size:(layer/2+1)*self.hidden_size]).T)
#with noise
_, _, cost, show_cost, error = GSN.build_gsn_given_hiddens(self.Xs, h_list, self.weights_list, self.bias_list, True, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.cost_function)
#without noise for reconstruction
x_sample_recon, _, _, recon_show_cost, _ = GSN.build_gsn_given_hiddens(self.Xs, h_list_recon, self.weights_list, self.bias_list, False, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.cost_function)
updates_train = updates_recurrent
updates_cost = updates_recurrent
#############
# COSTS #
#############
log.maybeLog(self.logger, '\nCost w.r.t p(X|...) at every step in the graph')
start_functions_time = time.time()
# if we are not using Hessian-free training create the normal sgd functions
if not self.hessian_free:
gradient = T.grad(cost, self.params)
gradient_buffer = [theano.shared(numpy.zeros(param.get_value().shape, dtype='float32')) for param in self.params]
m_gradient = [self.momentum * gb + (cast32(1) - self.momentum) * g for (gb, g) in zip(gradient_buffer, gradient)]
param_updates = [(param, param - self.learning_rate * mg) for (param, mg) in zip(self.params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
updates_train.update(updates)
log.maybeLog(self.logger, "rnn-gsn learn...")
self.f_learn = theano.function(inputs = [self.Xs],
updates = updates_train,
outputs = [show_cost, error],
on_unused_input='warn',
name='rnngsn_f_learn')
log.maybeLog(self.logger, "rnn-gsn cost...")
self.f_cost = theano.function(inputs = [self.Xs],
updates = updates_cost,
outputs = [show_cost, error],
on_unused_input='warn',
name='rnngsn_f_cost')
log.maybeLog(self.logger, "Training/cost functions done.")
# Denoise some numbers : show number, noisy number, predicted number, reconstructed number
log.maybeLog(self.logger, "Creating graph for noisy reconstruction function at checkpoints during training.")
self.f_recon = theano.function(inputs=[self.Xs],
outputs=[x_sample_recon[-1], recon_show_cost],
name='rnngsn_f_recon')
# a function to add salt and pepper noise
self.f_noise = theano.function(inputs = [self.X],
outputs = salt_and_pepper(self.X, self.input_salt_and_pepper),
name='rnngsn_f_noise')
# Sampling functions
log.maybeLog(self.logger, "Creating sampling function...")
if self.layers == 1:
self.f_sample = theano.function(inputs = [X_sample],
outputs = visible_pX_chain[-1],
name='rnngsn_f_sample_single_layer')
else:
self.f_sample = theano.function(inputs = self.network_state_input,
outputs = self.network_state_output + visible_pX_chain,
on_unused_input='warn',
name='rnngsn_f_sample')
log.maybeLog(self.logger, "Done compiling all functions.")
compilation_time = time.time() - start_functions_time
# Show the compile time with appropriate easy-to-read units.
log.maybeLog(self.logger, "Total compilation time took "+make_time_units_string(compilation_time)+".\n\n")
def __init__(self,
train_X=None,
valid_X=None,
test_X=None,
args=None,
logger=None):
# Output logger
self.logger = logger
self.outdir = args.get("output_path", defaults["output_path"])
if self.outdir[-1] != '/':
self.outdir = self.outdir + '/'
# Input data - make sure it is a list of shared datasets
self.train_X = raise_data_to_list(train_X)
self.valid_X = raise_data_to_list(valid_X)
self.test_X = raise_data_to_list(test_X)
# variables from the dataset that are used for initialization and image reconstruction
if train_X is None:
self.N_input = args.get("input_size")
if args.get("input_size") is None:
raise AssertionError(
"Please either specify input_size in the arguments or provide an example train_X for input dimensionality."
)
else:
self.N_input = train_X[0].eval().shape[1]
self.root_N_input = numpy.sqrt(self.N_input)
self.is_image = args.get('is_image', defaults['is_image'])
if self.is_image:
self.image_width = args.get('width', self.root_N_input)
self.image_height = args.get('height', self.root_N_input)
#######################################
# Network and training specifications #
#######################################
self.layers = args.get('layers',
defaults['layers']) # number hidden layers
self.walkbacks = args.get('walkbacks',
defaults['walkbacks']) # number of walkbacks
self.learning_rate = theano.shared(
cast32(args.get('learning_rate',
defaults['learning_rate']))) # learning rate
self.init_learn_rate = cast32(
args.get('learning_rate', defaults['learning_rate']))
self.momentum = theano.shared(
cast32(args.get('momentum',
defaults['momentum']))) # momentum term
self.annealing = cast32(args.get(
'annealing',
defaults['annealing'])) # exponential annealing coefficient
self.noise_annealing = cast32(
args.get('noise_annealing', defaults['noise_annealing'])
) # exponential noise annealing coefficient
self.batch_size = args.get('batch_size', defaults['batch_size'])
self.n_epoch = args.get('n_epoch', defaults['n_epoch'])
self.early_stop_threshold = args.get('early_stop_threshold',
defaults['early_stop_threshold'])
self.early_stop_length = args.get('early_stop_length',
defaults['early_stop_length'])
self.save_frequency = args.get('save_frequency',
defaults['save_frequency'])
self.noiseless_h1 = args.get('noiseless_h1', defaults["noiseless_h1"])
self.hidden_add_noise_sigma = theano.shared(
cast32(
args.get('hidden_add_noise_sigma',
defaults["hidden_add_noise_sigma"])))
self.input_salt_and_pepper = theano.shared(
cast32(
args.get('input_salt_and_pepper',
defaults["input_salt_and_pepper"])))
self.input_sampling = args.get('input_sampling',
defaults["input_sampling"])
self.vis_init = args.get('vis_init', defaults['vis_init'])
self.layer_sizes = [self.N_input] + [
args.get('hidden_size', defaults['hidden_size'])
] * self.layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.f_recon = None
self.f_noise = None
# Activation functions!
if args.get('hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for hiddens')
self.hidden_activation = args.get('hidden_activation')
elif args.get('hidden_act') is not None:
self.hidden_activation = get_activation_function(
args.get('hidden_act'))
log.maybeLog(
self.logger, 'Using {0!s} activation for hiddens'.format(
args.get('hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for hiddens")
self.hidden_activation = defaults['hidden_activation']
# Visible layer activation
if args.get('visible_activation') is not None:
log.maybeLog(self.logger,
'Using specified activation for visible layer')
self.visible_activation = args.get('visible_activation')
elif args.get('visible_act') is not None:
self.visible_activation = get_activation_function(
args.get('visible_act'))
log.maybeLog(
self.logger, 'Using {0!s} activation for visible layer'.format(
args.get('visible_act')))
else:
log.maybeLog(self.logger,
'Using default activation for visible layer')
self.visible_activation = defaults['visible_activation']
# Cost function!
if args.get('cost_function') is not None:
log.maybeLog(self.logger,
'\nUsing specified cost function for training\n')
self.cost_function = args.get('cost_function')
elif args.get('cost_funct') is not None:
self.cost_function = get_cost_function(args.get('cost_funct'))
log.maybeLog(
self.logger,
'Using {0!s} for cost function'.format(args.get('cost_funct')))
else:
log.maybeLog(self.logger,
'\nUsing default cost function for training\n')
self.cost_function = defaults['cost_function']
############################
# Theano variables and RNG #
############################
self.X = T.fmatrix('X') # for use in sampling
self.MRG = RNG_MRG.MRG_RandomStreams(1)
rng.seed(1)
###############
# Parameters! #
###############
# initialize a list of weights and biases based on layer_sizes for the GSN
if args.get('weights_list') is None:
self.weights_list = [
get_shared_weights(self.layer_sizes[layer],
self.layer_sizes[layer + 1],
name="W_{0!s}_{1!s}".format(
layer, layer + 1))
for layer in range(self.layers)
] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
else:
self.weights_list = args.get('weights_list')
if args.get('bias_list') is None:
self.bias_list = [
get_shared_bias(self.layer_sizes[layer],
name='b_' + str(layer))
for layer in range(self.layers + 1)
] # initialize each layer to 0's.
else:
self.bias_list = args.get('bias_list')
self.params = self.weights_list + self.bias_list
#################
# Build the GSN #
#################
log.maybeLog(self.logger,
"\nBuilding GSN graphs for training and testing")
# GSN for training - with noise
add_noise = True
p_X_chain, _ = build_gsn(
self.X, self.weights_list, self.bias_list, add_noise,
self.noiseless_h1, self.hidden_add_noise_sigma,
self.input_salt_and_pepper, self.input_sampling, self.MRG,
self.visible_activation, self.hidden_activation, self.walkbacks,
self.logger)
# GSN for reconstruction checks along the way - no noise
add_noise = False
p_X_chain_recon, _ = build_gsn(
self.X, self.weights_list, self.bias_list, add_noise,
self.noiseless_h1, self.hidden_add_noise_sigma,
self.input_salt_and_pepper, self.input_sampling, self.MRG,
self.visible_activation, self.hidden_activation, self.walkbacks,
self.logger)
#######################
# Costs and gradients #
#######################
log.maybeLog(
self.logger,
'Cost w.r.t p(X|...) at every step in the graph for the GSN')
gsn_costs = [self.cost_function(rX, self.X) for rX in p_X_chain]
show_gsn_cost = gsn_costs[-1] # for logging to show progress
gsn_cost = numpy.sum(gsn_costs)
gsn_costs_recon = [
self.cost_function(rX, self.X) for rX in p_X_chain_recon
]
show_gsn_cost_recon = gsn_costs_recon[-1]
log.maybeLog(self.logger, ["gsn params:", self.params])
# Stochastic gradient descent!
gradient = T.grad(gsn_cost, self.params)
gradient_buffer = [
theano.shared(numpy.zeros(param.get_value().shape,
dtype='float32'))
for param in self.params
]
m_gradient = [
self.momentum * gb + (cast32(1) - self.momentum) * g
for (gb, g) in zip(gradient_buffer, gradient)
]
param_updates = [(param, param - self.learning_rate * mg)
for (param, mg) in zip(self.params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
############
# Sampling #
############
# the input to the sampling function
X_sample = T.fmatrix("X_sampling")
self.network_state_input = [X_sample] + [
T.fmatrix("H_sampling_" + str(i + 1)) for i in range(self.layers)
]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
self.network_state_output = [X_sample] + self.network_state_input[1:]
visible_pX_chain = []
# ONE update
log.maybeLog(self.logger,
"Performing one walkback in network state sampling.")
update_layers(self.network_state_output, self.weights_list,
self.bias_list, visible_pX_chain, True,
self.noiseless_h1, self.hidden_add_noise_sigma,
self.input_salt_and_pepper, self.input_sampling,
self.MRG, self.visible_activation,
self.hidden_activation, self.logger)
#################################
# Create the functions #
#################################
log.maybeLog(self.logger, "Compiling functions...")
t = time.time()
self.f_learn = theano.function(inputs=[self.X],
updates=updates,
outputs=show_gsn_cost,
name='gsn_f_learn')
self.f_cost = theano.function(inputs=[self.X],
outputs=show_gsn_cost,
name='gsn_f_cost')
# used for checkpoints and testing - no noise in network
self.f_recon = theano.function(
inputs=[self.X],
outputs=[show_gsn_cost_recon, p_X_chain_recon[-1]],
name='gsn_f_recon')
self.f_noise = theano.function(inputs=[self.X],
outputs=salt_and_pepper(
self.X, self.input_salt_and_pepper),
name='gsn_f_noise')
if self.layers == 1:
self.f_sample = theano.function(inputs=[X_sample],
outputs=visible_pX_chain[-1],
name='gsn_f_sample_single_layer')
else:
# WHY IS THERE A WARNING????
# because the first odd layers are not used -> directly computed FROM THE EVEN layers
# unused input = warn
self.f_sample = theano.function(inputs=self.network_state_input,
outputs=self.network_state_output +
visible_pX_chain,
on_unused_input='warn',
name='gsn_f_sample')
log.maybeLog(
self.logger, "Compiling done. Took " +
make_time_units_string(time.time() - t) + ".\n")
def __init__(self, train_X=None, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None, args=None, logger=None):
# Output logger
self.logger = logger
self.outdir = args.get("output_path", defaults["output_path"])
if self.outdir[-1] != '/':
self.outdir = self.outdir+'/'
data.mkdir_p(self.outdir)
# Configuration
config_filename = self.outdir+'config'
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(args))
# Input data - make sure it is a list of shared datasets if it isn't. THIS WILL KEEP 'NONE' AS 'NONE' no need to worry :)
self.train_X = raise_to_list(train_X)
self.train_Y = raise_to_list(train_Y)
self.valid_X = raise_to_list(valid_X)
self.valid_Y = raise_to_list(valid_Y)
self.test_X = raise_to_list(test_X)
self.test_Y = raise_to_list(test_Y)
# variables from the dataset that are used for initialization and image reconstruction
if self.train_X is None:
self.N_input = args.get("input_size")
if args.get("input_size") is None:
raise AssertionError("Please either specify input_size in the arguments or provide an example train_X for input dimensionality.")
else:
self.N_input = self.train_X[0].get_value(borrow=True).shape[1]
self.is_image = args.get('is_image', defaults['is_image'])
if self.is_image:
(_h, _w) = closest_to_square_factors(self.N_input)
self.image_width = args.get('width', _w)
self.image_height = args.get('height', _h)
#######################################
# Network and training specifications #
#######################################
self.layers = args.get('layers', defaults['layers']) # number hidden layers
self.walkbacks = args.get('walkbacks', defaults['walkbacks']) # number of walkbacks
self.learning_rate = theano.shared(cast32(args.get('learning_rate', defaults['learning_rate']))) # learning rate
self.init_learn_rate = cast32(args.get('learning_rate', defaults['learning_rate']))
self.momentum = theano.shared(cast32(args.get('momentum', defaults['momentum']))) # momentum term
self.annealing = cast32(args.get('annealing', defaults['annealing'])) # exponential annealing coefficient
self.noise_annealing = cast32(args.get('noise_annealing', defaults['noise_annealing'])) # exponential noise annealing coefficient
self.batch_size = args.get('batch_size', defaults['batch_size'])
self.gsn_batch_size = args.get('gsn_batch_size', defaults['gsn_batch_size'])
self.n_epoch = args.get('n_epoch', defaults['n_epoch'])
self.early_stop_threshold = args.get('early_stop_threshold', defaults['early_stop_threshold'])
self.early_stop_length = args.get('early_stop_length', defaults['early_stop_length'])
self.save_frequency = args.get('save_frequency', defaults['save_frequency'])
self.noiseless_h1 = args.get('noiseless_h1', defaults["noiseless_h1"])
self.hidden_add_noise_sigma = theano.shared(cast32(args.get('hidden_add_noise_sigma', defaults["hidden_add_noise_sigma"])))
self.input_salt_and_pepper = theano.shared(cast32(args.get('input_salt_and_pepper', defaults["input_salt_and_pepper"])))
self.input_sampling = args.get('input_sampling', defaults["input_sampling"])
self.vis_init = args.get('vis_init', defaults['vis_init'])
self.initialize_gsn = args.get('initialize_gsn', defaults['initialize_gsn'])
self.hessian_free = args.get('hessian_free', defaults['hessian_free'])
self.hidden_size = args.get('hidden_size', defaults['hidden_size'])
self.layer_sizes = [self.N_input] + [self.hidden_size] * self.layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.recurrent_hidden_size = args.get('recurrent_hidden_size', defaults['recurrent_hidden_size'])
self.f_recon = None
self.f_noise = None
# Activation functions!
# For the GSN:
if args.get('hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for GSN hiddens')
self.hidden_activation = args.get('hidden_activation')
elif args.get('hidden_act') is not None:
self.hidden_activation = get_activation_function(args.get('hidden_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for GSN hiddens'.format(args.get('hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for GSN hiddens")
self.hidden_activation = defaults['hidden_activation']
# For the RNN:
if args.get('recurrent_hidden_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for RNN hiddens')
self.recurrent_hidden_activation = args.get('recurrent_hidden_activation')
elif args.get('recurrent_hidden_act') is not None:
self.recurrent_hidden_activation = get_activation_function(args.get('recurrent_hidden_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for RNN hiddens'.format(args.get('recurrent_hidden_act')))
else:
log.maybeLog(self.logger, "Using default activation for RNN hiddens")
self.recurrent_hidden_activation = defaults['recurrent_hidden_activation']
# Visible layer activation
if args.get('visible_activation') is not None:
log.maybeLog(self.logger, 'Using specified activation for visible layer')
self.visible_activation = args.get('visible_activation')
elif args.get('visible_act') is not None:
self.visible_activation = get_activation_function(args.get('visible_act'))
log.maybeLog(self.logger, 'Using {0!s} activation for visible layer'.format(args.get('visible_act')))
else:
log.maybeLog(self.logger, 'Using default activation for visible layer')
self.visible_activation = defaults['visible_activation']
# Cost function!
if args.get('cost_function') is not None:
log.maybeLog(self.logger, '\nUsing specified cost function for GSN training\n')
self.cost_function = args.get('cost_function')
elif args.get('cost_funct') is not None:
self.cost_function = get_cost_function(args.get('cost_funct'))
log.maybeLog(self.logger, 'Using {0!s} for cost function'.format(args.get('cost_funct')))
else:
log.maybeLog(self.logger, '\nUsing default cost function for GSN training\n')
self.cost_function = defaults['cost_function']
############################
# Theano variables and RNG #
############################
self.X = T.fmatrix('X') #single (batch) for training gsn
self.Xs = T.fmatrix('Xs') #sequence for training rnn-gsn
self.MRG = RNG_MRG.MRG_RandomStreams(1)
###############
# Parameters! #
###############
#gsn
self.weights_list = [get_shared_weights(self.layer_sizes[i], self.layer_sizes[i+1], name="W_{0!s}_{1!s}".format(i,i+1)) for i in range(self.layers)] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
self.bias_list = [get_shared_bias(self.layer_sizes[i], name='b_'+str(i)) for i in range(self.layers + 1)] # initialize each layer to 0's.
#recurrent
self.recurrent_to_gsn_weights_list = [get_shared_weights(self.recurrent_hidden_size, self.layer_sizes[layer], name="W_u_h{0!s}".format(layer)) for layer in range(self.layers+1) if layer%2 != 0]
self.W_u_u = get_shared_weights(self.recurrent_hidden_size, self.recurrent_hidden_size, name="W_u_u")
self.W_x_u = get_shared_weights(self.N_input, self.recurrent_hidden_size, name="W_x_u")
self.recurrent_bias = get_shared_bias(self.recurrent_hidden_size, name='b_u')
#lists for use with gradients
self.gsn_params = self.weights_list + self.bias_list
self.u_params = [self.W_u_u, self.W_x_u, self.recurrent_bias]
self.params = self.gsn_params + self.recurrent_to_gsn_weights_list + self.u_params
###########################################################
# load initial parameters of gsn #
###########################################################
self.train_gsn_first = False
if self.initialize_gsn:
params_to_load = 'gsn_params.pkl'
if not os.path.isfile(params_to_load):
self.train_gsn_first = True
else:
log.maybeLog(self.logger, "\nLoading existing GSN parameters\n")
loaded_params = cPickle.load(open(params_to_load,'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(self.weights_list)], self.weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(self.weights_list):], self.bias_list)]
if self.initialize_gsn:
self.gsn_args = {'weights_list': self.weights_list,
'bias_list': self.bias_list,
'hidden_activation': self.hidden_activation,
'visible_activation': self.visible_activation,
'cost_function': self.cost_function,
'layers': self.layers,
'walkbacks': self.walkbacks,
'hidden_size': self.hidden_size,
'learning_rate': args.get('learning_rate', defaults['learning_rate']),
'momentum': args.get('momentum', defaults['momentum']),
'annealing': self.annealing,
'noise_annealing': self.noise_annealing,
'batch_size': self.gsn_batch_size,
'n_epoch': self.n_epoch,
'early_stop_threshold': self.early_stop_threshold,
'early_stop_length': self.early_stop_length,
'save_frequency': self.save_frequency,
'noiseless_h1': self.noiseless_h1,
'hidden_add_noise_sigma': args.get('hidden_add_noise_sigma', defaults['hidden_add_noise_sigma']),
'input_salt_and_pepper': args.get('input_salt_and_pepper', defaults['input_salt_and_pepper']),
'input_sampling': self.input_sampling,
'vis_init': self.vis_init,
'output_path': self.outdir+'gsn/',
'is_image': self.is_image,
'input_size': self.N_input
}
############
# Sampling #
############
# the input to the sampling function
X_sample = T.fmatrix("X_sampling")
self.network_state_input = [X_sample] + [T.fmatrix("H_sampling_"+str(i+1)) for i in range(self.layers)]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
self.network_state_output = [X_sample] + self.network_state_input[1:]
visible_pX_chain = []
# ONE update
_add_noise = True
log.maybeLog(self.logger, "Performing one walkback in network state sampling.")
GSN.update_layers(self.network_state_output,
self.weights_list,
self.bias_list,
visible_pX_chain,
_add_noise,
self.noiseless_h1,
self.hidden_add_noise_sigma,
self.input_salt_and_pepper,
self.input_sampling,
self.MRG,
self.visible_activation,
self.hidden_activation,
self.logger)
#############################################
# Build the graphs for the RNN-GSN #
#############################################
# If `x_t` is given, deterministic recurrence to compute the u_t. Otherwise, first generate
def recurrent_step(x_t, u_tm1, add_noise):
# Make current guess for hiddens based on U
for i in range(self.layers):
if i%2 == 0:
log.maybeLog(self.logger, "Using {0!s} and {1!s}".format(self.recurrent_to_gsn_weights_list[(i+1)/2],self.bias_list[i+1]))
h_t = T.concatenate([self.hidden_activation(self.bias_list[i+1] + T.dot(u_tm1, self.recurrent_to_gsn_weights_list[(i+1)/2])) for i in range(self.layers) if i%2 == 0],axis=0)
generate = x_t is None
if generate:
pass
# Make a GSN to update U
# chain, hs = gsn.build_gsn(x_t, weights_list, bias_list, add_noise, state.noiseless_h1, state.hidden_add_noise_sigma, state.input_salt_and_pepper, state.input_sampling, MRG, visible_activation, hidden_activation, walkbacks, logger)
# htop_t = hs[-1]
# denoised_x_t = chain[-1]
# Update U
# ua_t = T.dot(denoised_x_t, W_x_u) + T.dot(htop_t, W_h_u) + T.dot(u_tm1, W_u_u) + recurrent_bias
ua_t = T.dot(x_t, self.W_x_u) + T.dot(u_tm1, self.W_u_u) + self.recurrent_bias
u_t = self.recurrent_hidden_activation(ua_t)
return None if generate else [ua_t, u_t, h_t]
log.maybeLog(self.logger, "\nCreating recurrent step scan.")
# For training, the deterministic recurrence is used to compute all the
# {h_t, 1 <= t <= T} given Xs. Conditional GSNs can then be trained
# in batches using those parameters.
u0 = T.zeros((self.recurrent_hidden_size,)) # initial value for the RNN hidden units
(ua, u, h_t), updates_recurrent = theano.scan(fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, True),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
log.maybeLog(self.logger, "Now for reconstruction sample without noise")
(_, _, h_t_recon), updates_recurrent_recon = theano.scan(fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, False),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
# put together the hiddens list
h_list = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer%2 != 0:
h_list.append(T.zeros_like(T.dot(h_list[-1], w)))
else:
h_list.append((h_t.T[(layer/2)*self.hidden_size:(layer/2+1)*self.hidden_size]).T)
h_list_recon = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer%2 != 0:
h_list_recon.append(T.zeros_like(T.dot(h_list_recon[-1], w)))
else:
h_list_recon.append((h_t_recon.T[(layer/2)*self.hidden_size:(layer/2+1)*self.hidden_size]).T)
#with noise
_, _, cost, show_cost, error = GSN.build_gsn_given_hiddens(self.Xs, h_list, self.weights_list, self.bias_list, True, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.cost_function)
#without noise for reconstruction
x_sample_recon, _, _, recon_show_cost, _ = GSN.build_gsn_given_hiddens(self.Xs, h_list_recon, self.weights_list, self.bias_list, False, self.noiseless_h1, self.hidden_add_noise_sigma, self.input_salt_and_pepper, self.input_sampling, self.MRG, self.visible_activation, self.hidden_activation, self.walkbacks, self.cost_function)
updates_train = updates_recurrent
updates_cost = updates_recurrent
#############
# COSTS #
#############
log.maybeLog(self.logger, '\nCost w.r.t p(X|...) at every step in the graph')
start_functions_time = time.time()
# if we are not using Hessian-free training create the normal sgd functions
if not self.hessian_free:
gradient = T.grad(cost, self.params)
gradient_buffer = [theano.shared(numpy.zeros(param.get_value().shape, dtype='float32')) for param in self.params]
m_gradient = [self.momentum * gb + (cast32(1) - self.momentum) * g for (gb, g) in zip(gradient_buffer, gradient)]
param_updates = [(param, param - self.learning_rate * mg) for (param, mg) in zip(self.params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
updates_train.update(updates)
log.maybeLog(self.logger, "rnn-gsn learn...")
self.f_learn = theano.function(inputs = [self.Xs],
updates = updates_train,
outputs = [show_cost, error],
on_unused_input='warn',
name='rnngsn_f_learn')
log.maybeLog(self.logger, "rnn-gsn cost...")
self.f_cost = theano.function(inputs = [self.Xs],
updates = updates_cost,
outputs = [show_cost, error],
on_unused_input='warn',
name='rnngsn_f_cost')
log.maybeLog(self.logger, "Training/cost functions done.")
# Denoise some numbers : show number, noisy number, predicted number, reconstructed number
log.maybeLog(self.logger, "Creating graph for noisy reconstruction function at checkpoints during training.")
self.f_recon = theano.function(inputs=[self.Xs],
outputs=[x_sample_recon[-1], recon_show_cost],
name='rnngsn_f_recon')
# a function to add salt and pepper noise
self.f_noise = theano.function(inputs = [self.X],
outputs = salt_and_pepper(self.X, self.input_salt_and_pepper),
name='rnngsn_f_noise')
# Sampling functions
log.maybeLog(self.logger, "Creating sampling function...")
if self.layers == 1:
self.f_sample = theano.function(inputs = [X_sample],
outputs = visible_pX_chain[-1],
name='rnngsn_f_sample_single_layer')
else:
self.f_sample = theano.function(inputs = self.network_state_input,
outputs = self.network_state_output + visible_pX_chain,
on_unused_input='warn',
name='rnngsn_f_sample')
log.maybeLog(self.logger, "Done compiling all functions.")
compilation_time = time.time() - start_functions_time
# Show the compile time with appropriate easy-to-read units.
log.maybeLog(self.logger, "Total compilation time took "+make_time_units_string(compilation_time)+".\n\n")
def experiment(state, outdir_base='./'):
rng.seed(1) # seed the numpy random generator
R.seed(1) # seed the other random generator (for reconstruction function indices)
# Initialize the output directories and files
data.mkdir_p(outdir_base)
outdir = outdir_base + "/" + state.dataset + "/"
data.mkdir_p(outdir)
logger = Logger(outdir)
train_convergence = outdir + "train_convergence.csv"
valid_convergence = outdir + "valid_convergence.csv"
test_convergence = outdir + "test_convergence.csv"
regression_train_convergence = outdir + "regression_train_convergence.csv"
regression_valid_convergence = outdir + "regression_valid_convergence.csv"
regression_test_convergence = outdir + "regression_test_convergence.csv"
init_empty_file(train_convergence)
init_empty_file(valid_convergence)
init_empty_file(test_convergence)
init_empty_file(regression_train_convergence)
init_empty_file(regression_valid_convergence)
init_empty_file(regression_test_convergence)
logger.log("----------MODEL 1, {0!s}--------------\n\n".format(state.dataset))
# load parameters from config file if this is a test
config_filename = outdir + 'config'
if state.test_model and 'config' in os.listdir(outdir):
config_vals = load_from_config(config_filename)
for CV in config_vals:
logger.log(CV)
if CV.startswith('test'):
logger.log('Do not override testing switch')
continue
try:
exec('state.' + CV) in globals(), locals()
except:
exec('state.' + CV.split('=')[0] + "='" + CV.split('=')[1] + "'") in globals(), locals()
else:
# Save the current configuration
# Useful for logs/experiments
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(state))
logger.log(state)
####################################################
# Load the data, train = train+valid, and sequence #
####################################################
artificial = False # internal flag to see if the dataset is one of my artificially-sequenced MNIST varieties.
if state.dataset == 'MNIST_1' or state.dataset == 'MNIST_2' or state.dataset == 'MNIST_3' or state.dataset == 'MNIST_4':
(train_X, train_Y), (valid_X, valid_Y), (test_X, test_Y) = data.load_mnist(state.data_path)
train_X = numpy.concatenate((train_X, valid_X))
train_Y = numpy.concatenate((train_Y, valid_Y))
artificial = True
try:
dataset = int(state.dataset.split('_')[1])
except:
raise AssertionError("artificial dataset number not recognized. Input was " + state.dataset)
else:
raise AssertionError("dataset not recognized.")
# transfer the datasets into theano shared variables
train_X = theano.shared(train_X)
train_Y = theano.shared(train_Y)
valid_X = theano.shared(valid_X)
valid_Y = theano.shared(valid_Y)
test_X = theano.shared(test_X)
test_Y = theano.shared(test_Y)
if artificial: # if it my MNIST sequence, appropriately sequence it.
logger.log('Sequencing MNIST data...')
logger.log(['train set size:', len(train_Y.eval())])
logger.log(['valid set size:', len(valid_Y.eval())])
logger.log(['test set size:', len(test_Y.eval())])
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
logger.log(['train set size:', len(train_Y.eval())])
logger.log(['valid set size:', len(valid_Y.eval())])
logger.log(['test set size:', len(test_Y.eval())])
logger.log('Sequencing done.\n')
# variables from the dataset that are used for initialization and image reconstruction
N_input = train_X.eval().shape[1]
root_N_input = numpy.sqrt(N_input)
# Network and training specifications
layers = state.layers # number hidden layers
walkbacks = state.walkbacks # number of walkbacks
sequence_window_size = state.sequence_window_size # number of previous hidden states to consider for the regression
layer_sizes = [N_input] + [state.hidden_size] * layers # layer sizes, from h0 to hK (h0 is the visible layer)
learning_rate = theano.shared(cast32(state.learning_rate)) # learning rate
regression_learning_rate = theano.shared(cast32(state.learning_rate)) # learning rate
annealing = cast32(state.annealing) # exponential annealing coefficient
momentum = theano.shared(cast32(state.momentum)) # momentum term
# Theano variables and RNG
X = T.fmatrix('X') # for use in sampling
Xs = [T.fmatrix(name="X_t") if i == 0 else T.fmatrix(name="X_{t-" + str(i) + "}") for i in range(
sequence_window_size + 1)] # for use in training - need one X variable for each input in the sequence history window, and what the current one should be
Xs_recon = [T.fvector(name="Xrecon_t") if i == 0 else T.fvector(name="Xrecon_{t-" + str(i) + "}") for i in range(
sequence_window_size + 1)] # for use in training - need one X variable for each input in the sequence history window, and what the current one should be
# sequence_graph_output_index = T.lscalar("i")
MRG = RNG_MRG.MRG_RandomStreams(1)
##############
# PARAMETERS #
##############
# initialize a list of weights and biases based on layer_sizes for the GSN
weights_list = [
get_shared_weights(layer_sizes[layer], layer_sizes[layer + 1], name="W_{0!s}_{1!s}".format(layer, layer + 1))
for layer in range(layers)] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
bias_list = [get_shared_bias(layer_sizes[layer], name='b_' + str(layer)) for layer in
range(layers + 1)] # initialize each layer to 0's.
# parameters for the regression - only need them for the odd layers in the network!
regression_weights_list = [
[get_shared_regression_weights(state.hidden_size, name="V_{t-" + str(window + 1) + "}_layer" + str(layer)) for
layer in range(layers + 1) if (layer % 2) != 0] for window in
range(sequence_window_size)] # initialize to identity matrix the size of hidden layer.
regression_bias_list = [get_shared_bias(state.hidden_size, name='vb_' + str(layer)) for layer in range(layers + 1)
if (layer % 2) != 0] # initialize to 0's.
# need initial biases (tau) as well for when there aren't sequence_window_size hiddens in the history.
tau_list = [
[get_shared_bias(state.hidden_size, name='tau_{t-' + str(window + 1) + "}_layer" + str(layer)) for layer in
range(layers + 1) if (layer % 2) != 0] for window in range(sequence_window_size)]
###########################################################
# load initial parameters of gsn to speed up my debugging #
###########################################################
params_to_load = 'gsn_params.pkl'
initialized_gsn = False
if os.path.isfile(params_to_load):
logger.log("\nLoading existing GSN parameters")
loaded_params = cPickle.load(open(params_to_load, 'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(weights_list)], weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(weights_list):], bias_list)]
initialized_gsn = True
########################
# ACTIVATION FUNCTIONS #
########################
if state.hidden_act == 'sigmoid':
logger.log('Using sigmoid activation for hiddens')
hidden_activation = T.nnet.sigmoid
elif state.hidden_act == 'rectifier':
logger.log('Using rectifier activation for hiddens')
hidden_activation = lambda x: T.maximum(cast32(0), x)
elif state.hidden_act == 'tanh':
logger.log('Using hyperbolic tangent activation for hiddens')
hidden_activation = lambda x: T.tanh(x)
else:
logger.log("Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid".format(
state.hidden_act))
raise AssertionError("Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid".format(
state.hidden_act))
if state.visible_act == 'sigmoid':
logger.log('Using sigmoid activation for visible layer')
visible_activation = T.nnet.sigmoid
elif state.visible_act == 'softmax':
logger.log('Using softmax activation for visible layer')
visible_activation = T.nnet.softmax
else:
logger.log(
"Did not recognize visible activation {0!s}, please use sigmoid or softmax".format(state.visible_act))
raise AssertionError(
"Did not recognize visible activation {0!s}, please use sigmoid or softmax".format(state.visible_act))
###############################################
# COMPUTATIONAL GRAPH HELPER METHODS FOR TGSN #
###############################################
def update_layers(hiddens, p_X_chain, noisy=True):
logger.log('odd layer updates')
update_odd_layers(hiddens, noisy)
logger.log('even layer updates')
update_even_layers(hiddens, p_X_chain, noisy)
logger.log('done full update.\n')
def update_layers_reverse(hiddens, p_X_chain, noisy=True):
logger.log('even layer updates')
update_even_layers(hiddens, p_X_chain, noisy)
logger.log('odd layer updates')
update_odd_layers(hiddens, noisy)
logger.log('done full update.\n')
# Odd layer update function
# just a loop over the odd layers
def update_odd_layers(hiddens, noisy):
for i in range(1, len(hiddens), 2):
logger.log(['updating layer', i])
simple_update_layer(hiddens, None, i, add_noise=noisy)
# Even layer update
# p_X_chain is given to append the p(X|...) at each full update (one update = odd update + even update)
def update_even_layers(hiddens, p_X_chain, noisy):
for i in range(0, len(hiddens), 2):
logger.log(['updating layer', i])
simple_update_layer(hiddens, p_X_chain, i, add_noise=noisy)
# The layer update function
# hiddens : list containing the symbolic theano variables [visible, hidden1, hidden2, ...]
# layer_update will modify this list inplace
# p_X_chain : list containing the successive p(X|...) at each update
# update_layer will append to this list
# i : the current layer being updated
# add_noise : pre (and post) activation gaussian noise flag
def simple_update_layer(hiddens, p_X_chain, i, add_noise=True):
# Compute the dot product, whatever layer
# If the visible layer X
if i == 0:
logger.log('using ' + str(weights_list[i]) + '.T')
hiddens[i] = T.dot(hiddens[i + 1], weights_list[i].T) + bias_list[i]
# If the top layer
elif i == len(hiddens) - 1:
logger.log(['using', weights_list[i - 1]])
hiddens[i] = T.dot(hiddens[i - 1], weights_list[i - 1]) + bias_list[i]
# Otherwise in-between layers
else:
logger.log(["using {0!s} and {1!s}.T".format(weights_list[i - 1], weights_list[i])])
# next layer : hiddens[i+1], assigned weights : W_i
# previous layer : hiddens[i-1], assigned weights : W_(i-1)
hiddens[i] = T.dot(hiddens[i + 1], weights_list[i].T) + T.dot(hiddens[i - 1], weights_list[i - 1]) + \
bias_list[i]
# Add pre-activation noise if NOT input layer
if i == 1 and state.noiseless_h1:
logger.log('>>NO noise in first hidden layer')
add_noise = False
# pre activation noise
if i != 0 and add_noise:
logger.log(['Adding pre-activation gaussian noise for layer', i])
hiddens[i] = add_gaussian_noise(hiddens[i], state.hidden_add_noise_sigma)
# ACTIVATION!
if i == 0:
logger.log('{} activation for visible layer'.format(state.visible_act))
hiddens[i] = visible_activation(hiddens[i])
else:
logger.log(['Hidden units {} activation for layer'.format(state.hidden_act), i])
hiddens[i] = hidden_activation(hiddens[i])
# post activation noise
# why is there post activation noise? Because there is already pre-activation noise, this just doubles the amount of noise between each activation of the hiddens.
# if i != 0 and add_noise:
# logger.log(['Adding post-activation gaussian noise for layer', i])
# hiddens[i] = add_gaussian(hiddens[i], state.hidden_add_noise_sigma)
# build the reconstruction chain if updating the visible layer X
if i == 0:
# if input layer -> append p(X|...)
p_X_chain.append(hiddens[i])
# sample from p(X|...) - SAMPLING NEEDS TO BE CORRECT FOR INPUT TYPES I.E. FOR BINARY MNIST SAMPLING IS BINOMIAL. real-valued inputs should be gaussian
if state.input_sampling:
logger.log('Sampling from input')
sampled = MRG.binomial(p=hiddens[i], size=hiddens[i].shape, dtype='float32')
else:
logger.log('>>NO input sampling')
sampled = hiddens[i]
# add noise
sampled = salt_and_pepper(sampled, state.input_salt_and_pepper)
# set input layer
hiddens[i] = sampled
def perform_regression_step(hiddens, sequence_history):
logger.log(["Sequence history length:", len(sequence_history)])
# only need to work over the odd layers of the hiddens
odd_layers = [i for i in range(len(hiddens)) if (i % 2) != 0]
# depending on the size of the sequence history, it could be 0, 1, 2, 3, ... sequence_window_size
for (hidden_index, regression_index) in zip(odd_layers, range(len(odd_layers))):
terms_used = []
sequence_terms = []
for history_index in range(sequence_window_size):
if history_index < len(sequence_history):
# dot product with history term
sequence_terms.append(T.dot(sequence_history[history_index][regression_index],
regression_weights_list[history_index][regression_index]))
terms_used.append(regression_weights_list[history_index][regression_index])
else:
# otherwise, no history for necessary spot, so use the tau
sequence_terms.append(tau_list[history_index][regression_index])
terms_used.append(tau_list[history_index][regression_index])
if len(sequence_terms) > 0:
sequence_terms.append(regression_bias_list[regression_index])
terms_used.append(regression_bias_list[regression_index])
logger.log(["REGRESSION for hidden layer {0!s} using:".format(hidden_index), terms_used])
hiddens[hidden_index] = numpy.sum(sequence_terms)
def build_gsn_graph(x, noiseflag):
p_X_chain = []
if noiseflag:
X_init = salt_and_pepper(x, state.input_salt_and_pepper)
else:
X_init = x
# init hiddens with zeros
hiddens = [X_init]
for w in weights_list:
hiddens.append(T.zeros_like(T.dot(hiddens[-1], w)))
# The layer update scheme
logger.log(["Building the gsn graph :", walkbacks, "updates"])
for i in range(walkbacks):
logger.log("GSN Walkback {!s}/{!s}".format(i + 1, walkbacks))
update_layers(hiddens, p_X_chain, noisy=noiseflag)
return p_X_chain
def build_sequence_graph(xs, noiseflag):
predicted_X_chains = []
p_X_chains = []
sequence_history = []
# The layer update scheme
logger.log(["Building the regression graph :", len(Xs), "updates"])
for x_index in range(len(xs)):
x = xs[x_index]
# Predict what the current X should be
''' hidden layer init '''
pred_hiddens = [T.zeros_like(x)]
for w in weights_list:
# init with zeros
pred_hiddens.append(T.zeros_like(T.dot(pred_hiddens[-1], w)))
logger.log("Performing regression step!")
perform_regression_step(pred_hiddens, sequence_history) # do the regression!
logger.log("\n")
predicted_X_chain = []
for i in range(walkbacks):
logger.log("Prediction Walkback {!s}/{!s}".format(i + 1, walkbacks))
update_layers_reverse(pred_hiddens, predicted_X_chain,
noisy=False) # no noise in the prediction because x_prediction can't be recovered from x anyway
predicted_X_chains.append(predicted_X_chain)
# Now do the actual GSN step and add it to the sequence history
# corrupt x if noisy
if noiseflag:
X_init = salt_and_pepper(x, state.input_salt_and_pepper)
else:
X_init = x
''' hidden layer init '''
hiddens = [T.zeros_like(x)]
for w in weights_list:
# init with zeros
hiddens.append(T.zeros_like(T.dot(hiddens[-1], w)))
# # substitute some of the zero layers for what was predicted - need to advance the prediction by 1 layer so it is the evens
# update_even_layers(pred_hiddens,[],noisy=False)
# for i in [layer for layer in range(len(hiddens)) if (layer%2 == 0)]:
# hiddens[i] = pred_hiddens[i]
hiddens[0] = X_init
chain = []
for i in range(walkbacks):
logger.log("GSN walkback {!s}/{!s}".format(i + 1, walkbacks))
update_layers(hiddens, chain, noisy=noiseflag)
# Append the p_X_chain
p_X_chains.append(chain)
# Append the odd layers of the hiddens to the sequence history
sequence_history.append([hiddens[layer] for layer in range(len(hiddens)) if (layer % 2) != 0])
# select the prediction and reconstruction from the lists
# prediction_chain = T.stacklists(predicted_X_chains)[sequence_graph_output_index]
# reconstruction_chain = T.stacklists(p_X_chains)[sequence_graph_output_index]
return predicted_X_chains, p_X_chains
##############################################
# Build the training graph for the GSN #
##############################################
logger.log("\nBuilding GSN graphs")
p_X_chain_init = build_gsn_graph(X, noiseflag=True)
predicted_X_chain_gsns, p_X_chains = build_sequence_graph(Xs, noiseflag=True)
predicted_X_chain_gsn = predicted_X_chain_gsns[-1]
p_X_chain = p_X_chains[-1]
###############################################
# Build the training graph for the regression #
###############################################
logger.log("\nBuilding regression graph")
# no noise! noise is only used as regularization for GSN stage
predicted_X_chains_regression, _ = build_sequence_graph(Xs, noiseflag=False)
predicted_X_chain = predicted_X_chains_regression[-1]
######################
# COST AND GRADIENTS #
######################
if state.cost_funct == 'binary_crossentropy':
logger.log('\nUsing binary cross-entropy cost!')
cost_function = lambda x, y: T.mean(T.nnet.binary_crossentropy(x, y))
elif state.cost_funct == 'square':
logger.log("\nUsing square error cost!")
# cost_function = lambda x,y: T.log(T.mean(T.sqr(x-y)))
cost_function = lambda x, y: T.log(T.sum(T.pow((x - y), 2)))
else:
raise AssertionError(
"Did not recognize cost function {0!s}, please use binary_crossentropy or square".format(state.cost_funct))
logger.log('Cost w.r.t p(X|...) at every step in the graph for the TGSN')
gsn_costs_init = [cost_function(rX, X) for rX in p_X_chain_init]
show_gsn_cost_init = gsn_costs_init[-1]
gsn_cost_init = numpy.sum(gsn_costs_init)
gsn_init_mse = T.mean(T.sqr(p_X_chain_init[-1] - X), axis=0)
gsn_init_error = T.mean(gsn_init_mse)
# gsn_costs = T.mean(T.mean(T.nnet.binary_crossentropy(p_X_chain, T.stacklists(Xs)[sequence_graph_output_index]),2),1)
gsn_costs = [cost_function(rX, Xs[-1]) for rX in predicted_X_chain_gsn]
show_gsn_cost = gsn_costs[-1]
gsn_cost = T.sum(gsn_costs)
gsn_mse = T.mean(T.sqr(predicted_X_chain_gsn[-1] - Xs[-1]), axis=0)
gsn_error = T.mean(gsn_mse)
gsn_params = weights_list + bias_list
logger.log(["gsn params:", gsn_params])
# l2 regularization
# regression_regularization_cost = T.sum([T.sum(recurrent_weights ** 2) for recurrent_weights in regression_weights_list])
regression_regularization_cost = 0
regression_costs = [cost_function(rX, Xs[-1]) for rX in predicted_X_chain]
show_regression_cost = regression_costs[-1]
regression_cost = T.sum(regression_costs) + state.regularize_weight * regression_regularization_cost
regression_mse = T.mean(T.sqr(predicted_X_chain[-1] - Xs[-1]), axis=0)
regression_error = T.mean(regression_mse)
# only using the odd layers update -> even-indexed parameters in the list because it starts at v1
# need to flatten the regression list -> couldn't immediately find the python method so here is the implementation
regression_weights_flattened = []
for weights in regression_weights_list:
regression_weights_flattened.extend(weights)
tau_flattened = []
for tau in tau_list:
tau_flattened.extend(tau)
regression_params = regression_weights_flattened + regression_bias_list # + tau_flattened
logger.log(["regression params:", regression_params])
logger.log("creating functions...")
t = time.time()
gradient_init = T.grad(gsn_cost_init, gsn_params)
gradient_buffer_init = [theano.shared(numpy.zeros(param.get_value().shape, dtype='float32')) for param in
gsn_params]
m_gradient_init = [momentum * gb + (cast32(1) - momentum) * g for (gb, g) in
zip(gradient_buffer_init, gradient_init)]
param_updates_init = [(param, param - learning_rate * mg) for (param, mg) in zip(gsn_params, m_gradient_init)]
gradient_buffer_updates_init = zip(gradient_buffer_init, m_gradient_init)
updates_init = OrderedDict(param_updates_init + gradient_buffer_updates_init)
gsn_f_learn_init = theano.function(inputs=[X],
updates=updates_init,
outputs=[show_gsn_cost_init, gsn_init_error])
gsn_f_cost_init = theano.function(inputs=[X],
outputs=[show_gsn_cost_init, gsn_init_error])
gradient = T.grad(gsn_cost, gsn_params)
gradient_buffer = [theano.shared(numpy.zeros(param.get_value().shape, dtype='float32')) for param in gsn_params]
m_gradient = [momentum * gb + (cast32(1) - momentum) * g for (gb, g) in zip(gradient_buffer, gradient)]
param_updates = [(param, param - learning_rate * mg) for (param, mg) in zip(gsn_params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
gsn_f_cost = theano.function(inputs=Xs,
outputs=[show_gsn_cost, gsn_error])
gsn_f_learn = theano.function(inputs=Xs,
updates=updates,
outputs=[show_gsn_cost, gsn_error])
regression_gradient = T.grad(regression_cost, regression_params)
regression_gradient_buffer = [theano.shared(numpy.zeros(rparam.get_value().shape, dtype='float32')) for rparam in
regression_params]
regression_m_gradient = [momentum * rgb + (cast32(1) - momentum) * rg for (rgb, rg) in
zip(regression_gradient_buffer, regression_gradient)]
regression_param_updates = [(rparam, rparam - regression_learning_rate * rmg) for (rparam, rmg) in
zip(regression_params, regression_m_gradient)]
regression_gradient_buffer_updates = zip(regression_gradient_buffer, regression_m_gradient)
regression_updates = OrderedDict(regression_param_updates + regression_gradient_buffer_updates)
regression_f_cost = theano.function(inputs=Xs,
outputs=[show_regression_cost, regression_error])
regression_f_learn = theano.function(inputs=Xs,
updates=regression_updates,
outputs=[show_regression_cost, regression_error])
logger.log("functions done. took " + make_time_units_string(time.time() - t) + ".\n")
############################################################################################
# Denoise some numbers : show number, noisy number, predicted number, reconstructed number #
############################################################################################
# Recompile the graph without noise for reconstruction function
# The layer update scheme
logger.log("Creating graph for noisy reconstruction function at checkpoints during training.")
predicted_X_chains_R, p_X_chains_R = build_sequence_graph(Xs_recon, noiseflag=False)
predicted_X_chain_R = predicted_X_chains_R[-1]
p_X_chain_R = p_X_chains_R[-1]
f_recon = theano.function(inputs=Xs_recon, outputs=[predicted_X_chain_R[-1], p_X_chain_R[-1]])
# Now do the same but for the GSN in the initial run
p_X_chain_R = build_gsn_graph(X, noiseflag=False)
f_recon_init = theano.function(inputs=[X], outputs=p_X_chain_R[-1])
############
# Sampling #
############
f_noise = theano.function(inputs=[X], outputs=salt_and_pepper(X, state.input_salt_and_pepper))
# the input to the sampling function
network_state_input = [X] + [T.fmatrix() for i in range(layers)]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
# network_state_output = network_state_input
network_state_output = [X] + network_state_input[1:]
visible_pX_chain = []
# ONE update
logger.log("Performing one walkback in network state sampling.")
update_layers(network_state_output, visible_pX_chain, noisy=True)
if layers == 1:
f_sample_simple = theano.function(inputs=[X], outputs=visible_pX_chain[-1])
# WHY IS THERE A WARNING????
# because the first odd layers are not used -> directly computed FROM THE EVEN layers
# unused input = warn
f_sample2 = theano.function(inputs=network_state_input, outputs=network_state_output + visible_pX_chain,
on_unused_input='warn')
def sample_some_numbers_single_layer():
x0 = test_X.get_value()[7:8]
samples = [x0]
x = f_noise(x0)
for i in range(399):
x = f_sample_simple(x)
samples.append(x)
x = numpy.random.binomial(n=1, p=x, size=x.shape).astype('float32')
x = f_noise(x)
return numpy.vstack(samples)
def sampling_wrapper(NSI):
# * is the "splat" operator: It takes a list as input, and expands it into actual positional arguments in the function call.
out = f_sample2(*NSI)
NSO = out[:len(network_state_output)]
vis_pX_chain = out[len(network_state_output):]
return NSO, vis_pX_chain
def sample_some_numbers(N=400):
# The network's initial state
init_vis = test_X.get_value()[7:8]
noisy_init_vis = f_noise(init_vis)
network_state = [
[noisy_init_vis] + [numpy.zeros((1, len(b.get_value())), dtype='float32') for b in bias_list[1:]]]
visible_chain = [init_vis]
noisy_h0_chain = [noisy_init_vis]
for i in range(N - 1):
# feed the last state into the network, compute new state, and obtain visible units expectation chain
net_state_out, vis_pX_chain = sampling_wrapper(network_state[-1])
# append to the visible chain
visible_chain += vis_pX_chain
# append state output to the network state chain
network_state.append(net_state_out)
noisy_h0_chain.append(net_state_out[0])
return numpy.vstack(visible_chain), numpy.vstack(noisy_h0_chain)
def plot_samples(epoch_number, iteration):
to_sample = time.time()
if layers == 1:
# one layer model
V = sample_some_numbers_single_layer()
else:
V, _ = sample_some_numbers()
img_samples = PIL.Image.fromarray(tile_raster_images(V, (root_N_input, root_N_input), (20, 20)))
fname = outdir + 'samples_iteration_' + str(iteration) + '_epoch_' + str(epoch_number) + '.png'
img_samples.save(fname)
logger.log('Took ' + str(time.time() - to_sample) + ' to sample 400 numbers')
#############################
# Save the model parameters #
#############################
def save_params_to_file(name, n, gsn_params, iteration):
pass
logger.log('saving parameters...')
save_path = outdir + name + '_params_iteration_' + str(iteration) + '_epoch_' + str(n) + '.pkl'
f = open(save_path, 'wb')
try:
cPickle.dump(gsn_params, f, protocol=cPickle.HIGHEST_PROTOCOL)
finally:
f.close()
def save_params(params):
values = [param.get_value(borrow=True) for param in params]
return values
def restore_params(params, values):
for i in range(len(params)):
params[i].set_value(values[i])
################
# GSN TRAINING #
################
def train_GSN(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
logger.log('----------------TRAINING GSN FOR ITERATION ' + str(iteration) + "--------------\n")
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
if iteration == 0:
learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
logger.log(['learning rate:', learning_rate.get_value()])
logger.log(['train X size:', str(train_X.shape.eval())])
logger.log(['valid X size:', str(valid_X.shape.eval())])
logger.log(['test X size:', str(test_X.shape.eval())])
if state.vis_init:
bias_list[0].set_value(logit(numpy.clip(0.9, 0.001, train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter, '\t'])
# shuffle the data
# data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
# train
train_costs = []
train_errors = []
if iteration == 0:
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
x = train_X.get_value(borrow=True)[i * batch_size: (i + 1) * batch_size]
cost, error = gsn_f_learn_init(x)
train_costs.append([cost])
train_errors.append([error])
else:
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
xs = [train_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, error = gsn_f_learn(*_ins)
train_costs.append(cost)
train_errors.append(error)
train_costs = numpy.mean(train_costs)
train_errors = numpy.mean(train_errors)
logger.append(['Train: ', trunc(train_costs), trunc(train_errors), '\t'])
with open(train_convergence, 'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
# valid
valid_costs = []
if iteration == 0:
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
x = valid_X.get_value(borrow=True)[i * batch_size: (i + 1) * batch_size]
cost, _ = gsn_f_cost_init(x)
valid_costs.append([cost])
else:
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
xs = [valid_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
costs, _ = gsn_f_cost(*_ins)
valid_costs.append(costs)
valid_costs = numpy.mean(valid_costs)
logger.append(['Valid: ', trunc(valid_costs), '\t'])
with open(valid_convergence, 'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
# test
test_costs = []
test_errors = []
if iteration == 0:
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
x = test_X.get_value(borrow=True)[i * batch_size: (i + 1) * batch_size]
cost, error = gsn_f_cost_init(x)
test_costs.append([cost])
test_errors.append([error])
else:
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
xs = [test_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
costs, errors = gsn_f_cost(*_ins)
test_costs.append(costs)
test_errors.append(errors)
test_costs = numpy.mean(test_costs)
test_errors = numpy.mean(test_errors)
logger.append(['Test: ', trunc(test_costs), trunc(test_errors), '\t'])
with open(test_convergence, 'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
# check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * state.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(gsn_params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(gsn_params, best_params)
save_params_to_file('gsn', counter, gsn_params, iteration)
logger.log(["next learning rate should be", learning_rate.get_value() * annealing])
timing = time.time() - t
times.append(timing)
logger.append('time: ' + make_time_units_string(timing))
logger.log('remaining: ' + make_time_units_string((n_epoch - counter) * numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100
if iteration == 0:
random_idx = numpy.array(R.sample(range(len(test_X.get_value())), n_examples))
numbers = test_X.get_value()[random_idx]
noisy_numbers = f_noise(test_X.get_value()[random_idx])
reconstructed = f_recon_init(noisy_numbers)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack(
[numbers[i * 10: (i + 1) * 10], noisy_numbers[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input), (10, 30)))
else:
n_examples = n_examples + sequence_window_size
# Checking reconstruction
# grab 100 numbers in the sequence from the test set
nums = test_X.get_value()[range(n_examples)]
noisy_nums = f_noise(test_X.get_value()[range(n_examples)])
reconstructed_prediction = []
reconstructed = []
for i in range(n_examples):
if i >= sequence_window_size:
xs = [noisy_nums[i - x] for x in range(len(Xs))]
xs.reverse()
_ins = xs # + [sequence_window_size]
_outs = f_recon(*_ins)
prediction = _outs[0]
reconstruction = _outs[1]
reconstructed_prediction.append(prediction)
reconstructed.append(reconstruction)
nums = nums[sequence_window_size:]
noisy_nums = noisy_nums[sequence_window_size:]
reconstructed_prediction = numpy.array(reconstructed_prediction)
reconstructed = numpy.array(reconstructed)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i * 10: (i + 1) * 10], noisy_nums[i * 10: (i + 1) * 10],
reconstructed_prediction[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input), (10, 40)))
# epoch_number = reduce(lambda x,y : x + y, ['_'] * (4-len(str(counter)))) + str(counter)
number_reconstruction.save(
outdir + 'gsn_number_reconstruction_iteration_' + str(iteration) + '_epoch_' + str(
counter) + '.png')
# sample_numbers(counter, 'seven')
plot_samples(counter, iteration)
# save gsn_params
save_params_to_file('gsn', counter, gsn_params, iteration)
# ANNEAL!
new_lr = learning_rate.get_value() * annealing
learning_rate.set_value(new_lr)
# 10k samples
logger.log('Generating 10,000 samples')
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir + 'samples.npy'
numpy.save(f_samples, samples)
logger.log('saved digits')
#######################
# REGRESSION TRAINING #
#######################
def train_regression(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
logger.log('-------------TRAINING REGRESSION FOR ITERATION {0!s}-------------'.format(iteration))
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
best_cost = float('inf')
best_params = None
patience = 0
if iteration == 0:
regression_learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
logger.log(['learning rate:', regression_learning_rate.get_value()])
logger.log(['train X size:', str(train_X.shape.eval())])
logger.log(['valid X size:', str(valid_X.shape.eval())])
logger.log(['test X size:', str(test_X.shape.eval())])
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter, '\t'])
# shuffle the data
# data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
# train
train_costs = []
train_errors = []
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
xs = [train_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, error = regression_f_learn(*_ins)
# print trunc(cost)
# print [numpy.asarray(a) for a in f_check(*_ins)]
train_costs.append(cost)
train_errors.append(error)
train_costs = numpy.mean(train_costs)
train_errors = numpy.mean(train_errors)
logger.append(['rTrain: ', trunc(train_costs), trunc(train_errors), '\t'])
with open(regression_train_convergence, 'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
# valid
valid_costs = []
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
xs = [valid_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, _ = regression_f_cost(*_ins)
valid_costs.append(cost)
valid_costs = numpy.mean(valid_costs)
logger.append(['rValid: ', trunc(valid_costs), '\t'])
with open(regression_valid_convergence, 'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
# test
test_costs = []
test_errors = []
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
xs = [test_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, error = regression_f_cost(*_ins)
test_costs.append(cost)
test_errors.append(error)
test_costs = numpy.mean(test_costs)
test_errors = numpy.mean(test_errors)
logger.append(['rTest: ', trunc(test_costs), trunc(test_errors), '\t'])
with open(regression_test_convergence, 'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
# check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * state.early_stop_threshold:
patience = 0
best_cost = cost
# keep the best params so far
best_params = save_params(regression_params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(regression_params, best_params)
save_params_to_file('regression', counter, regression_params, iteration)
logger.log(["next learning rate should be", regression_learning_rate.get_value() * annealing])
timing = time.time() - t
times.append(timing)
logger.append('time: ' + make_time_units_string(timing))
logger.log('remaining: ' + make_time_units_string((n_epoch - counter) * numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100 + sequence_window_size
# Checking reconstruction
# grab 100 numbers in the sequence from the test set
nums = test_X.get_value()[range(n_examples)]
noisy_nums = f_noise(test_X.get_value()[range(n_examples)])
reconstructed_prediction = []
reconstructed = []
for i in range(n_examples):
if i >= sequence_window_size:
xs = [noisy_nums[i - x] for x in range(len(Xs))]
xs.reverse()
_ins = xs # + [sequence_window_size]
_outs = f_recon(*_ins)
prediction = _outs[0]
reconstruction = _outs[1]
reconstructed_prediction.append(prediction)
reconstructed.append(reconstruction)
nums = nums[sequence_window_size:]
noisy_nums = noisy_nums[sequence_window_size:]
reconstructed_prediction = numpy.array(reconstructed_prediction)
reconstructed = numpy.array(reconstructed)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i * 10: (i + 1) * 10], noisy_nums[i * 10: (i + 1) * 10],
reconstructed_prediction[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input), (10, 40)))
# epoch_number = reduce(lambda x,y : x + y, ['_'] * (4-len(str(counter)))) + str(counter)
number_reconstruction.save(
outdir + 'regression_number_reconstruction_iteration_' + str(iteration) + '_epoch_' + str(
counter) + '.png')
# save gsn_params
save_params_to_file('regression', counter, regression_params, iteration)
# ANNEAL!
new_r_lr = regression_learning_rate.get_value() * annealing
regression_learning_rate.set_value(new_r_lr)
#####################
# STORY 1 ALGORITHM #
#####################
# alternate training the gsn and training the regression
for iteration in range(state.max_iterations):
# if iteration is 0 and initialized_gsn is False:
# train_regression(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y)
# else:
# train_GSN(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y)
# train_regression(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y)
train_GSN(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y)
train_regression(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y)
def __init__(self,
train_X=None,
train_Y=None,
valid_X=None,
valid_Y=None,
test_X=None,
test_Y=None,
args=None,
logger=None):
# Output logger
self.logger = logger
self.outdir = args.get("output_path", defaults["output_path"])
if self.outdir[-1] != '/':
self.outdir = self.outdir + '/'
# Input data
self.train_X = train_X
self.train_Y = train_Y
self.valid_X = valid_X
self.valid_Y = valid_Y
self.test_X = test_X
self.test_Y = test_Y
# variables from the dataset that are used for initialization and image reconstruction
if train_X is None:
self.N_input = args.get("input_size")
if args.get("input_size") is None:
raise AssertionError(
"Please either specify input_size in the arguments or provide an example train_X for input dimensionality."
)
else:
self.N_input = train_X.eval().shape[1]
self.root_N_input = numpy.sqrt(self.N_input)
self.is_image = args.get('is_image', defaults['is_image'])
if self.is_image:
self.image_width = args.get('width', self.root_N_input)
self.image_height = args.get('height', self.root_N_input)
#######################################
# Network and training specifications #
#######################################
self.gsn_layers = args.get(
'gsn_layers', defaults['gsn_layers']) # number hidden layers
self.walkbacks = args.get('walkbacks',
defaults['walkbacks']) # number of walkbacks
self.learning_rate = theano.shared(
cast32(args.get('learning_rate',
defaults['learning_rate']))) # learning rate
self.init_learn_rate = cast32(
args.get('learning_rate', defaults['learning_rate']))
self.momentum = theano.shared(
cast32(args.get('momentum',
defaults['momentum']))) # momentum term
self.annealing = cast32(args.get(
'annealing',
defaults['annealing'])) # exponential annealing coefficient
self.noise_annealing = cast32(
args.get('noise_annealing', defaults['noise_annealing'])
) # exponential noise annealing coefficient
self.batch_size = args.get('batch_size', defaults['batch_size'])
self.gsn_batch_size = args.get('gsn_batch_size',
defaults['gsn_batch_size'])
self.n_epoch = args.get('n_epoch', defaults['n_epoch'])
self.early_stop_threshold = args.get('early_stop_threshold',
defaults['early_stop_threshold'])
self.early_stop_length = args.get('early_stop_length',
defaults['early_stop_length'])
self.save_frequency = args.get('save_frequency',
defaults['save_frequency'])
self.noiseless_h1 = args.get('noiseless_h1', defaults["noiseless_h1"])
self.hidden_add_noise_sigma = theano.shared(
cast32(
args.get('hidden_add_noise_sigma',
defaults["hidden_add_noise_sigma"])))
self.input_salt_and_pepper = theano.shared(
cast32(
args.get('input_salt_and_pepper',
defaults["input_salt_and_pepper"])))
self.input_sampling = args.get('input_sampling',
defaults["input_sampling"])
self.vis_init = args.get('vis_init', defaults['vis_init'])
self.load_params = args.get('load_params', defaults['load_params'])
self.hessian_free = args.get('hessian_free', defaults['hessian_free'])
self.layer_sizes = [self.N_input] + [
args.get('hidden_size', defaults['hidden_size'])
] * self.gsn_layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.recurrent_hidden_size = args.get(
'recurrent_hidden_size', defaults['recurrent_hidden_size'])
self.top_layer_sizes = [self.recurrent_hidden_size] + [
args.get('hidden_size', defaults['hidden_size'])
] * self.gsn_layers # layer sizes, from h0 to hK (h0 is the visible layer)
self.f_recon = None
self.f_noise = None
# Activation functions!
# For the GSN:
if args.get('hidden_activation') is not None:
log.maybeLog(self.logger,
'Using specified activation for GSN hiddens')
self.hidden_activation = args.get('hidden_activation')
elif args.get('hidden_act') == 'sigmoid':
log.maybeLog(self.logger,
'Using sigmoid activation for GSN hiddens')
self.hidden_activation = T.nnet.sigmoid
elif args.get('hidden_act') == 'rectifier':
log.maybeLog(self.logger,
'Using rectifier activation for GSN hiddens')
self.hidden_activation = lambda x: T.maximum(cast32(0), x)
elif args.get('hidden_act') == 'tanh':
log.maybeLog(
self.logger,
'Using hyperbolic tangent activation for GSN hiddens')
self.hidden_activation = lambda x: T.tanh(x)
elif args.get('hidden_act') is not None:
log.maybeLog(
self.logger,
"Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for GSN hiddens"
.format(args.get('hidden_act')))
raise NotImplementedError(
"Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for GSN hiddens"
.format(args.get('hidden_act')))
else:
log.maybeLog(self.logger,
"Using default activation for GSN hiddens")
self.hidden_activation = defaults['hidden_activation']
# For the RNN:
if args.get('recurrent_hidden_activation') is not None:
log.maybeLog(self.logger,
'Using specified activation for RNN hiddens')
self.recurrent_hidden_activation = args.get(
'recurrent_hidden_activation')
elif args.get('recurrent_hidden_act') == 'sigmoid':
log.maybeLog(self.logger,
'Using sigmoid activation for RNN hiddens')
self.recurrent_hidden_activation = T.nnet.sigmoid
elif args.get('recurrent_hidden_act') == 'rectifier':
log.maybeLog(self.logger,
'Using rectifier activation for RNN hiddens')
self.recurrent_hidden_activation = lambda x: T.maximum(
cast32(0), x)
elif args.get('recurrent_hidden_act') == 'tanh':
log.maybeLog(
self.logger,
'Using hyperbolic tangent activation for RNN hiddens')
self.recurrent_hidden_activation = lambda x: T.tanh(x)
elif args.get('recurrent_hidden_act') is not None:
log.maybeLog(
self.logger,
"Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for RNN hiddens"
.format(args.get('hidden_act')))
raise NotImplementedError(
"Did not recognize hidden activation {0!s}, please use tanh, rectifier, or sigmoid for RNN hiddens"
.format(args.get('hidden_act')))
else:
log.maybeLog(self.logger,
"Using default activation for RNN hiddens")
self.recurrent_hidden_activation = defaults[
'recurrent_hidden_activation']
# Visible layer activation
if args.get('visible_activation') is not None:
log.maybeLog(self.logger,
'Using specified activation for visible layer')
self.visible_activation = args.get('visible_activation')
elif args.get('visible_act') == 'sigmoid':
log.maybeLog(self.logger,
'Using sigmoid activation for visible layer')
self.visible_activation = T.nnet.sigmoid
elif args.get('visible_act') == 'softmax':
log.maybeLog(self.logger,
'Using softmax activation for visible layer')
self.visible_activation = T.nnet.softmax
elif args.get('visible_act') is not None:
log.maybeLog(
self.logger,
"Did not recognize visible activation {0!s}, please use sigmoid or softmax"
.format(args.get('visible_act')))
raise NotImplementedError(
"Did not recognize visible activation {0!s}, please use sigmoid or softmax"
.format(args.get('visible_act')))
else:
log.maybeLog(self.logger,
'Using default activation for visible layer')
self.visible_activation = defaults['visible_activation']
# Cost function!
if args.get('cost_function') is not None:
log.maybeLog(self.logger,
'\nUsing specified cost function for GSN training\n')
self.cost_function = args.get('cost_function')
elif args.get('cost_funct') == 'binary_crossentropy':
log.maybeLog(self.logger, '\nUsing binary cross-entropy cost!\n')
self.cost_function = lambda x, y: T.mean(
T.nnet.binary_crossentropy(x, y))
elif args.get('cost_funct') == 'square':
log.maybeLog(self.logger, "\nUsing square error cost!\n")
#cost_function = lambda x,y: T.log(T.mean(T.sqr(x-y)))
self.cost_function = lambda x, y: T.log(T.sum(T.pow((x - y), 2)))
elif args.get('cost_funct') is not None:
log.maybeLog(
self.logger,
"\nDid not recognize cost function {0!s}, please use binary_crossentropy or square\n"
.format(args.get('cost_funct')))
raise NotImplementedError(
"Did not recognize cost function {0!s}, please use binary_crossentropy or square"
.format(args.get('cost_funct')))
else:
log.maybeLog(self.logger,
'\nUsing default cost function for GSN training\n')
self.cost_function = defaults['cost_function']
############################
# Theano variables and RNG #
############################
self.X = T.fmatrix('X') #single (batch) for training gsn
self.Xs = T.fmatrix('Xs') #sequence for training rnn
self.MRG = RNG_MRG.MRG_RandomStreams(1)
###############
# Parameters! #
###############
#visible gsn
self.weights_list = [
get_shared_weights(self.layer_sizes[i],
self.layer_sizes[i + 1],
name="W_{0!s}_{1!s}".format(i, i + 1))
for i in range(self.gsn_layers)
] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
self.bias_list = [
get_shared_bias(self.layer_sizes[i], name='b_' + str(i))
for i in range(self.gsn_layers + 1)
] # initialize each layer to 0's.
#recurrent
self.recurrent_to_gsn_weights_list = [
get_shared_weights(self.recurrent_hidden_size,
self.layer_sizes[layer],
name="W_u_h{0!s}".format(layer))
for layer in range(self.gsn_layers + 1) if layer % 2 != 0
]
self.W_u_u = get_shared_weights(self.recurrent_hidden_size,
self.recurrent_hidden_size,
name="W_u_u")
self.W_ins_u = get_shared_weights(args.get('hidden_size',
defaults['hidden_size']),
self.recurrent_hidden_size,
name="W_ins_u")
self.recurrent_bias = get_shared_bias(self.recurrent_hidden_size,
name='b_u')
#top layer gsn
self.top_weights_list = [
get_shared_weights(self.top_layer_sizes[i],
self.top_layer_sizes[i + 1],
name="Wtop_{0!s}_{1!s}".format(i, i + 1))
for i in range(self.gsn_layers)
] # initialize each layer to uniform sample from sqrt(6. / (n_in + n_out))
self.top_bias_list = [
get_shared_bias(self.top_layer_sizes[i], name='btop_' + str(i))
for i in range(self.gsn_layers + 1)
] # initialize each layer to 0's.
#lists for use with gradients
self.gsn_params = self.weights_list + self.bias_list
self.u_params = [self.W_u_u, self.W_ins_u, self.recurrent_bias]
self.top_params = self.top_weights_list + self.top_bias_list
self.params = self.gsn_params + self.recurrent_to_gsn_weights_list + self.u_params + self.top_params
###################################################
# load initial parameters #
###################################################
if self.load_params:
params_to_load = 'gsn_params.pkl'
log.maybeLog(self.logger, "\nLoading existing GSN parameters\n")
loaded_params = cPickle.load(open(params_to_load, 'r'))
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[:len(self.weights_list)], self.weights_list)
]
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[len(self.weights_list):], self.bias_list)
]
params_to_load = 'rnn_params.pkl'
log.maybeLog(self.logger, "\nLoading existing RNN parameters\n")
loaded_params = cPickle.load(open(params_to_load, 'r'))
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[:len(self.recurrent_to_gsn_weights_list)],
self.recurrent_to_gsn_weights_list)
]
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[len(self.recurrent_to_gsn_weights_list
):len(self.recurrent_to_gsn_weights_list
) + 1], self.W_u_u)
]
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[len(self.recurrent_to_gsn_weights_list) +
1:len(self.recurrent_to_gsn_weights_list) +
2], self.W_ins_u)
]
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[len(self.recurrent_to_gsn_weights_list) +
2:], self.recurrent_bias)
]
params_to_load = 'top_gsn_params.pkl'
log.maybeLog(self.logger,
"\nLoading existing top level GSN parameters\n")
loaded_params = cPickle.load(open(params_to_load, 'r'))
[
p.set_value(lp.get_value(borrow=False))
for lp, p in zip(loaded_params[:len(self.top_weights_list)],
self.top_weights_list)
]
[
p.set_value(lp.get_value(borrow=False))
for lp, p in zip(loaded_params[len(self.top_weights_list):],
self.top_bias_list)
]
self.gsn_args = {
'weights_list':
self.weights_list,
'bias_list':
self.bias_list,
'hidden_activation':
self.hidden_activation,
'visible_activation':
self.visible_activation,
'cost_function':
self.cost_function,
'layers':
self.gsn_layers,
'walkbacks':
self.walkbacks,
'hidden_size':
args.get('hidden_size', defaults['hidden_size']),
'learning_rate':
args.get('learning_rate', defaults['learning_rate']),
'momentum':
args.get('momentum', defaults['momentum']),
'annealing':
self.annealing,
'noise_annealing':
self.noise_annealing,
'batch_size':
self.gsn_batch_size,
'n_epoch':
self.n_epoch,
'early_stop_threshold':
self.early_stop_threshold,
'early_stop_length':
self.early_stop_length,
'save_frequency':
self.save_frequency,
'noiseless_h1':
self.noiseless_h1,
'hidden_add_noise_sigma':
args.get('hidden_add_noise_sigma',
defaults['hidden_add_noise_sigma']),
'input_salt_and_pepper':
args.get('input_salt_and_pepper',
defaults['input_salt_and_pepper']),
'input_sampling':
self.input_sampling,
'vis_init':
self.vis_init,
'output_path':
self.outdir + 'gsn/',
'is_image':
self.is_image,
'input_size':
self.N_input
}
self.top_gsn_args = {
'weights_list':
self.top_weights_list,
'bias_list':
self.top_bias_list,
'hidden_activation':
self.hidden_activation,
'visible_activation':
self.recurrent_hidden_activation,
'cost_function':
self.cost_function,
'layers':
self.gsn_layers,
'walkbacks':
self.walkbacks,
'hidden_size':
args.get('hidden_size', defaults['hidden_size']),
'learning_rate':
args.get('learning_rate', defaults['learning_rate']),
'momentum':
args.get('momentum', defaults['momentum']),
'annealing':
self.annealing,
'noise_annealing':
self.noise_annealing,
'batch_size':
self.gsn_batch_size,
'n_epoch':
self.n_epoch,
'early_stop_threshold':
self.early_stop_threshold,
'early_stop_length':
self.early_stop_length,
'save_frequency':
self.save_frequency,
'noiseless_h1':
self.noiseless_h1,
'hidden_add_noise_sigma':
args.get('hidden_add_noise_sigma',
defaults['hidden_add_noise_sigma']),
'input_salt_and_pepper':
args.get('input_salt_and_pepper',
defaults['input_salt_and_pepper']),
'input_sampling':
self.input_sampling,
'vis_init':
self.vis_init,
'output_path':
self.outdir + 'top_gsn/',
'is_image':
False,
'input_size':
self.recurrent_hidden_size
}
############
# Sampling #
############
# the input to the sampling function
X_sample = T.fmatrix("X_sampling")
self.network_state_input = [X_sample] + [
T.fmatrix("H_sampling_" + str(i + 1))
for i in range(self.gsn_layers)
]
# "Output" state of the network (noisy)
# initialized with input, then we apply updates
self.network_state_output = [X_sample] + self.network_state_input[1:]
visible_pX_chain = []
# ONE update
log.maybeLog(self.logger,
"Performing one walkback in network state sampling.")
generative_stochastic_network.update_layers(
self.network_state_output, self.weights_list, self.bias_list,
visible_pX_chain, True, self.noiseless_h1,
self.hidden_add_noise_sigma, self.input_salt_and_pepper,
self.input_sampling, self.MRG, self.visible_activation,
self.hidden_activation, self.logger)
##############################################
# Build the graphs for the SEN #
##############################################
# If `x_t` is given, deterministic recurrence to compute the u_t. Otherwise, first generate
def recurrent_step(x_t, u_tm1, add_noise):
# Make current guess for hiddens based on U
for i in range(self.gsn_layers):
if i % 2 == 0:
log.maybeLog(
self.logger, "Using {0!s} and {1!s}".format(
self.recurrent_to_gsn_weights_list[(i + 1) / 2],
self.bias_list[i + 1]))
h_t = T.concatenate([
self.hidden_activation(self.bias_list[i + 1] + T.dot(
u_tm1, self.recurrent_to_gsn_weights_list[(i + 1) / 2]))
for i in range(self.gsn_layers) if i % 2 == 0
],
axis=0)
# Make a GSN to update U
_, hs = generative_stochastic_network.build_gsn(
x_t, self.weights_list, self.bias_list, add_noise,
self.noiseless_h1, self.hidden_add_noise_sigma,
self.input_salt_and_pepper, self.input_sampling, self.MRG,
self.visible_activation, self.hidden_activation,
self.walkbacks, self.logger)
htop_t = hs[-1]
ins_t = htop_t
ua_t = T.dot(ins_t, self.W_ins_u) + T.dot(
u_tm1, self.W_u_u) + self.recurrent_bias
u_t = self.recurrent_hidden_activation(ua_t)
return [ua_t, u_t, h_t]
log.maybeLog(self.logger, "\nCreating recurrent step scan.")
# For training, the deterministic recurrence is used to compute all the
# {h_t, 1 <= t <= T} given Xs. Conditional GSNs can then be trained
# in batches using those parameters.
u0 = T.zeros((self.recurrent_hidden_size,
)) # initial value for the RNN hidden units
(ua, u, h_t), updates_recurrent = theano.scan(
fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, True),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
log.maybeLog(self.logger,
"Now for reconstruction sample without noise")
(_, _, h_t_recon), updates_recurrent_recon = theano.scan(
fn=lambda x_t, u_tm1, *_: recurrent_step(x_t, u_tm1, False),
sequences=self.Xs,
outputs_info=[None, u0, None],
non_sequences=self.params)
# put together the hiddens list
h_list = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer % 2 != 0:
h_list.append(T.zeros_like(T.dot(h_list[-1], w)))
else:
h_list.append(
(h_t.T[(layer / 2) * self.hidden_size:(layer / 2 + 1) *
self.hidden_size]).T)
h_list_recon = [T.zeros_like(self.Xs)]
for layer, w in enumerate(self.weights_list):
if layer % 2 != 0:
h_list_recon.append(T.zeros_like(T.dot(h_list_recon[-1], w)))
else:
h_list_recon.append(
(h_t_recon.T[(layer / 2) *
self.hidden_size:(layer / 2 + 1) *
self.hidden_size]).T)
#with noise
_, cost, show_cost = generative_stochastic_network.build_gsn_given_hiddens(
self.Xs, h_list, self.weights_list, self.bias_list, True,
self.noiseless_h1, self.hidden_add_noise_sigma,
self.input_salt_and_pepper, self.input_sampling, self.MRG,
self.visible_activation, self.hidden_activation, self.walkbacks,
self.cost_function, self.logger)
#without noise for reconstruction
x_sample_recon, _, _ = generative_stochastic_network.build_gsn_given_hiddens(
self.Xs, h_list_recon, self.weights_list, self.bias_list, False,
self.noiseless_h1, self.hidden_add_noise_sigma,
self.input_salt_and_pepper, self.input_sampling, self.MRG,
self.visible_activation, self.hidden_activation, self.walkbacks,
self.cost_function, self.logger)
updates_train = updates_recurrent
updates_cost = updates_recurrent
#############
# COSTS #
#############
log.maybeLog(self.logger,
'\nCost w.r.t p(X|...) at every step in the graph')
start_functions_time = time.time()
# if we are not using Hessian-free training create the normal sgd functions
if not self.hessian_free:
gradient = T.grad(cost, self.params)
gradient_buffer = [
theano.shared(
numpy.zeros(param.get_value().shape, dtype='float32'))
for param in self.params
]
m_gradient = [
self.momentum * gb + (cast32(1) - self.momentum) * g
for (gb, g) in zip(gradient_buffer, gradient)
]
param_updates = [(param, param - self.learning_rate * mg)
for (param, mg) in zip(self.params, m_gradient)]
gradient_buffer_updates = zip(gradient_buffer, m_gradient)
updates = OrderedDict(param_updates + gradient_buffer_updates)
updates_train.update(updates)
log.maybeLog(self.logger, "rnn-gsn learn...")
self.f_learn = theano.function(inputs=[self.Xs],
updates=updates_train,
outputs=show_cost,
on_unused_input='warn',
name='rnngsn_f_learn')
log.maybeLog(self.logger, "rnn-gsn cost...")
self.f_cost = theano.function(inputs=[self.Xs],
updates=updates_cost,
outputs=show_cost,
on_unused_input='warn',
name='rnngsn_f_cost')
log.maybeLog(self.logger, "Training/cost functions done.")
# Denoise some numbers : show number, noisy number, predicted number, reconstructed number
log.maybeLog(
self.logger,
"Creating graph for noisy reconstruction function at checkpoints during training."
)
self.f_recon = theano.function(inputs=[self.Xs],
outputs=x_sample_recon[-1],
updates=updates_recurrent_recon,
name='rnngsn_f_recon')
# a function to add salt and pepper noise
self.f_noise = theano.function(inputs=[self.X],
outputs=salt_and_pepper(
self.X, self.input_salt_and_pepper),
name='rnngsn_f_noise')
# Sampling functions
log.maybeLog(self.logger, "Creating sampling function...")
if self.gsn_layers == 1:
self.f_sample = theano.function(
inputs=[X_sample],
outputs=visible_pX_chain[-1],
name='rnngsn_f_sample_single_layer')
else:
# WHY IS THERE A WARNING????
# because the first odd layers are not used -> directly computed FROM THE EVEN layers
# unused input = warn
self.f_sample = theano.function(inputs=self.network_state_input,
outputs=self.network_state_output +
visible_pX_chain,
on_unused_input='warn',
name='rnngsn_f_sample')
log.maybeLog(self.logger, "Done compiling all functions.")
compilation_time = time.time() - start_functions_time
# Show the compile time with appropriate easy-to-read units.
log.maybeLog(
self.logger, "Total compilation time took " +
make_time_units_string(compilation_time) + ".\n\n")
