def build_model(args):
x = tensor.tensor3('features', dtype=floatX)
y = tensor.tensor3('targets', dtype=floatX)
linear = Linear(input_dim=1, output_dim=4 * args.units)
rnn = LSTM(dim=args.units, activation=Tanh())
linear2 = Linear(input_dim=args.units, output_dim=1)
prediction = Tanh().apply(linear2.apply(rnn.apply(linear.apply(x))))
prediction = prediction[:-1, :, :]
# SquaredError does not work on 3D tensor
y = y.reshape((y.shape[0] * y.shape[1], y.shape[2]))
prediction = prediction.reshape((prediction.shape[0] * prediction.shape[1],
prediction.shape[2]))
cost = SquaredError().apply(y, prediction)
# Initialization
linear.weights_init = IsotropicGaussian(0.1)
linear2.weights_init = IsotropicGaussian(0.1)
linear.biases_init = Constant(0)
linear2.biases_init = Constant(0)
rnn.weights_init = Orthogonal()
return cost
def decoder_network(latent_sample, latent_dim=J): # bernoulli case hidden2 = get_typical_layer(latent_sample, latent_dim, 500, Logistic()) hidden2_to_output = Linear(name="last", input_dim=500, output_dim=784) hidden2_to_output.weights_init = IsotropicGaussian(0.01) hidden2_to_output.biases_init = Constant(0) hidden2_to_output.initialize() return Logistic().apply(hidden2_to_output.apply(hidden2))
def get_presoft(h, args):
output_size = get_output_size(args.dataset)
# If args.skip_connections: dim = args.layers * args.state_dim
# else: dim = args.state_dim
use_all_states = args.skip_connections or args.skip_output or (args.rnn_type in ["clockwork", "soft"])
output_layer = Linear(
input_dim=use_all_states * args.layers *
args.state_dim + (1 - use_all_states) * args.state_dim,
output_dim=output_size, name="output_layer")
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
presoft = output_layer.apply(h)
if not has_indices(args.dataset):
presoft = Tanh().apply(presoft)
presoft.name = 'presoft'
return presoft
def get_presoft(h, args):
output_size = get_output_size(args.dataset)
# If args.skip_connections: dim = args.layers * args.state_dim
# else: dim = args.state_dim
use_all_states = args.skip_connections or args.skip_output or (
args.rnn_type in ["clockwork", "soft"])
output_layer = Linear(
input_dim=use_all_states * args.layers * args.state_dim +
(1 - use_all_states) * args.state_dim,
output_dim=output_size,
name="output_layer")
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
presoft = output_layer.apply(h)
if not has_indices(args.dataset):
presoft = Tanh().apply(presoft)
presoft.name = 'presoft'
return presoft
def build_model_hard(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(state_dim)
lookup = LookupTable(length=vocab_size, dim=state_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names,
input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence([lookup.apply]))
transitions = [SimpleRecurrent(dim=state_dim, activation=Tanh())]
for i in range(layers - 1):
mlp = MLP(activations=[Logistic()],
dims=[2 * state_dim, 1],
weights_init=initialization.IsotropicGaussian(0.1),
biases_init=initialization.Constant(0),
name="mlp_" + str(i))
transitions.append(
HardGatedRecurrent(dim=state_dim, mlp=mlp, activation=Tanh()))
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# dim = layers * state_dim
output_layer = Linear(input_dim=layers * state_dim,
output_dim=vocab_size,
name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs' + suffix] = pre_rnn
init_states[d] = theano.shared(numpy.zeros(
(args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
kwargs['states' + suffix] = init_states[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# Now we have correctly:
# h = [state_1, state_2, state_3 ...]
# Save all the last states
last_states = {}
for d in range(layers):
last_states[d] = h[d][-1, :, :]
# Concatenate all the states
if layers > 1:
h = tensor.concatenate(h, axis=2)
h.name = "hidden_state"
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(), presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates
def build_model_vanilla(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(state_dim)
lookup = LookupTable(length=vocab_size, dim=state_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names, input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence(
[lookup.apply]))
transitions = [SimpleRecurrent(dim=state_dim, activation=Tanh())
for _ in range(layers)]
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# If skip_connections: dim = layers * state_dim
# else: dim = state_dim
output_layer = Linear(
input_dim=skip_connections * layers *
state_dim + (1 - skip_connections) * state_dim,
output_dim=vocab_size, name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs'] = pre_rnn
init_states[d] = theano.shared(
numpy.zeros((args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
kwargs['states' + suffix] = init_states[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# We have
# h = [state, state_1, state_2 ...] if layers > 1
# h = state if layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
last_states = {}
if layers > 1:
# Save all the last states
for d in range(layers):
last_states[d] = h[d][-1, :, :]
if skip_connections:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
last_states[0] = h[-1, :, :]
h.name = "hidden_state"
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(),
presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates
def build_model_soft(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(state_dim)
lookup = LookupTable(length=vocab_size, dim=state_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names, input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence(
[lookup.apply]))
transitions = [SimpleRecurrent(dim=state_dim, activation=Tanh())]
# Build the MLP
dims = [2 * state_dim]
activations = []
for i in range(args.mlp_layers):
activations.append(Rectifier())
dims.append(state_dim)
# Activation of the last layer of the MLP
if args.mlp_activation == "logistic":
activations.append(Logistic())
elif args.mlp_activation == "rectifier":
activations.append(Rectifier())
elif args.mlp_activation == "hard_logistic":
activations.append(HardLogistic())
else:
assert False
# Output of MLP has dimension 1
dims.append(1)
for i in range(layers - 1):
mlp = MLP(activations=activations, dims=dims,
weights_init=initialization.IsotropicGaussian(0.1),
biases_init=initialization.Constant(0),
name="mlp_" + str(i))
transitions.append(
SoftGatedRecurrent(dim=state_dim,
mlp=mlp,
activation=Tanh()))
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# dim = layers * state_dim
output_layer = Linear(
input_dim=layers * state_dim,
output_dim=vocab_size, name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs' + suffix] = pre_rnn
init_states[d] = theano.shared(
numpy.zeros((args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
kwargs['states' + suffix] = init_states[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# Now we have:
# h = [state, state_1, gate_value_1, state_2, gate_value_2, state_3, ...]
# Extract gate_values
gate_values = h[2::2]
new_h = [h[0]]
new_h.extend(h[1::2])
h = new_h
# Now we have:
# h = [state, state_1, state_2, ...]
# gate_values = [gate_value_1, gate_value_2, gate_value_3]
for i, gate_value in enumerate(gate_values):
gate_value.name = "gate_value_" + str(i)
# Save all the last states
last_states = {}
for d in range(layers):
last_states[d] = h[d][-1, :, :]
# Concatenate all the states
if layers > 1:
h = tensor.concatenate(h, axis=2)
h.name = "hidden_state"
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(),
presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates, gate_values
#对cost函数进行正则化
#选择需要计算的参数 W1 为第一层所有线性转换的 W ，W2 为第二层所有线性转换的W
W1,W2 = VariableFilter(roles = [WEIGHT])(cg.variables)
#正则化公式定义，此处使用的是L2正则化
cost = cost + 0.005 * (W1 ** 2).sum() + 0.005 * (W2 ** 2).sum()
cost.name = 'cost_with_regularization'
#定义一个多层神经网络，层与层之间的计算公式已经被之前定义。
#激活函数集activations定义了每一层的非线性转换函数，多层感知器每一层的输出都包含了两部分，第一部分是线性计算，然后将线性计算的结果进行非线性转换
#x是多层感知器的输入
mlp = MLP(activations = [Rectifier(),Softmax()], dims = [784, 100, 10]).apply(x)
#定义完整个神经网络的流程后，需要设置其线性转换的参数的初始值。
input_to_hidden.weights_init = IsotropicGaussian(0.01)
input_to_hidden.biases_init = Constant(0);
hidden_to_output.weights_init = IsotropicGaussian(0.01)
hidden_to_output.biases_init = Constant(0)
#对设置进行初始化设置，必须要做这一步，否则之前的设置都没有用
input_to_hidden.initialize()
hidden_to_output.initialize()
print W1.get_value()
#然后开始进行模型训练，这里使用现有的内置的数据集 MNIST，如果想要使用别的数据集，需要使用fuel对数据进行预处理
mnist = MNIST(("train",))
#定义迭代计算的方式，使用mini-batch的方法计算，每一次mini-batch使用1024条数据。以此获得数据流，data_stream
data_stream = Flatten(DataStream.default_stream(mnist, iteration_scheme = SequentialScheme(mnist.num_examples, batch_size = 256)))
#定义优化函数的最优值计算方法，这边使用SGD来做
#algorithm = GradientDescent(cost = cost, parameters = [cg.parameters], step_rule = Scale(learning_rate = 0.01))
x = tensor.tensor4('features')
flat_x = tensor.flatten(x, outdim=2)
flat_x_noise = flat_x + rng.normal(size=flat_x.shape, std=0.5)
y = tensor.imatrix('targets')
flat_y = tensor.flatten(y, outdim=1)
rect = Rectifier()
mlp = MLP(dims=[784, 1200, 1200, 200], activations=[rect, rect, rect], seed=10)
mlp.weights_init = Uniform(0.0, 0.01)
mlp.biases_init = Constant(0.0)
mlp.initialize()
lin = Linear(200, 10, use_bias=True)
lin.weights_init = Uniform(0.0, 0.01)
lin.biases_init = Constant(0.0)
lin.initialize()
train_out = lin.apply(mlp.apply(flat_x))
test_out = lin.apply(mlp.apply(flat_x))
sm = Softmax(name='softmax')
loss = sm.categorical_cross_entropy(flat_y, train_out).mean()
loss.name = 'nll'
misclass = MisclassificationRate().apply(flat_y, train_out)
misclass.name = 'misclass'
test_loss = sm.categorical_cross_entropy(flat_y, test_out).mean()
test_loss.name = 'nll'
test_misclass = MisclassificationRate().apply(flat_y, test_out)
test_misclass.name = 'misclass'
dims=[n_latent, n_hidden, n_vis]).apply(z)
# Define the cost
KL_term = -0.5 * (1 + encoder_lognu -T.exp(encoder_lognu) - encoder_mu ** 2 ).sum(axis=1)
reconstruction_term = (x * T.log(decoder_p) +
(1 - x) * T.log(1 - decoder_p)).sum(axis=1)
cost = (KL_term -reconstruction_term).mean()
cost.name = 'negative_log_likelihood'
# Initialize the parameters
encoder_layer_1.weights_init = Uniform(width=12. / (n_vis + n_hidden))
encoder_layer_1.biases_init = Constant(0)
encoder_mu.weights_init = Uniform(width=12. / (n_hidden + n_latent))
encoder_mu.biases_init = Constant(0)
encoder_lognu.weights_init = Uniform(width=12. / (n_hidden + n_latent))
encoder_lognu.biases_init = Constant(0)
mnist = MNIST(("train",))
data_stream = Flatten(DataStream.default_stream(
mnist,
iteration_scheme=SequentialScheme(mnist.num_examples, batch_size=100)))
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
def build_model_lstm(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
virtual_dim = 4 * state_dim
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(virtual_dim)
lookup = LookupTable(length=vocab_size, dim=virtual_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
# Make sure time_length is what we need
fork = Fork(output_names=output_names,
input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence([lookup.apply]))
transitions = [
LSTM(dim=state_dim, activation=Tanh()) for _ in range(layers)
]
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# If skip_connections: dim = layers * state_dim
# else: dim = state_dim
output_layer = Linear(input_dim=skip_connections * layers * state_dim +
(1 - skip_connections) * state_dim,
output_dim=vocab_size,
name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
init_cells = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs'] = pre_rnn
init_states[d] = theano.shared(numpy.zeros(
(args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
init_cells[d] = theano.shared(numpy.zeros(
(args.mini_batch_size, state_dim)).astype(floatX),
name='cell0_%d' % d)
kwargs['states' + suffix] = init_states[d]
kwargs['cells' + suffix] = init_cells[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# h = [state, cell, in, forget, out, state_1,
# cell_1, in_1, forget_1, out_1 ...]
last_states = {}
last_cells = {}
for d in range(layers):
last_states[d] = h[5 * d][-1, :, :]
last_cells[d] = h[5 * d + 1][-1, :, :]
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
updates.append((init_cells[d], last_states[d]))
# h = [state, cell, in, forget, out, state_1,
# cell_1, in_1, forget_1, out_1 ...]
# Extract the values
in_gates = h[2::5]
forget_gates = h[3::5]
out_gates = h[4::5]
gate_values = {
"in_gates": in_gates,
"forget_gates": forget_gates,
"out_gates": out_gates
}
h = h[::5]
# Now we have correctly:
# h = [state, state_1, state_2 ...] if layers > 1
# h = [state] if layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
if layers > 1:
if skip_connections:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
h = h[0]
h.name = "hidden_state"
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(), presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
# Dont initialize as Orthogonal if we are about to load new parameters
if args.load_path is not None:
rnn.weights_init = initialization.Constant(0)
else:
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates, gate_values
x = tensor.tensor4('features')
flat_x = tensor.flatten(x, outdim=2)
flat_x_noise = flat_x + rng.normal(size=flat_x.shape, std=0.5)
y = tensor.imatrix('targets')
flat_y = tensor.flatten(y, outdim=1)
rect = Rectifier()
mlp = MLP(dims=[784, 1200, 1200, 200], activations=[rect, rect, rect], seed=10)
mlp.weights_init = Uniform(0.0, 0.01)
mlp.biases_init = Constant(0.0)
mlp.initialize()
lin = Linear(200, 10, use_bias=True)
lin.weights_init = Uniform(0.0, 0.01)
lin.biases_init = Constant(0.0)
lin.initialize()
train_out = lin.apply(mlp.apply(flat_x))
test_out = lin.apply(mlp.apply(flat_x))
sm = Softmax(name='softmax')
loss = sm.categorical_cross_entropy(flat_y, train_out).mean()
loss.name = 'nll'
misclass = MisclassificationRate().apply(flat_y, train_out)
misclass.name = 'misclass'
test_loss = sm.categorical_cross_entropy(flat_y, test_out).mean()
test_loss.name = 'nll'
test_misclass = MisclassificationRate().apply(flat_y, test_out)
test_misclass.name = 'misclass'
def get_typical_layer(input_layer, input_dim, output_dim, transformation=Logistic()): layer = Linear(input_dim=input_dim, output_dim=output_dim) layer.weights_init = IsotropicGaussian(0.01) layer.biases_init = Constant(0) layer.initialize() return transformation.apply(layer.apply(input_layer))
