def test_shape():
x = T.tensor3()
x_flat_2_mat = T.flatten(x, 2)
x_flat_2_vec = T.flatten(x, 1)
flat_f = theano.function([x], [x_flat_2_mat, x_flat_2_vec])
flat_mat_val, flat_vec_val = flat_f(tensor3_val)
print 'flatten to 2-d array:'
print flat_mat_val
print 'flatten to 1-d array:'
print flat_vec_val
x_mat = T.matrix()
x_mat_2_t3 = T.reshape(x_mat, (2, 2, 2))
x_mat_2_vec = T.reshape(x_mat, (8,))
reshape_f = theano.function([x_mat], [x_mat_2_t3, x_mat_2_vec])
"""
t3_shape = T.lvector()
vec_shape = T.lvector()
x_mat_2_t3 = T.reshape(x_mat, t3_shape, 3)
x_mat_2_vec = T.reshape(x_mat, vec_shape, 1)
reshape_f = theano.function([x_mat, t3_shape, vec_shape], [x_mat_2_t3, x_mat_2_vec])
"""
mat_2_t3_val, mat_2_vec_val = reshape_f(flat_mat_val)
print 'reshape 2-d array to 3-d array:'
print mat_2_t3_val
print 'reshape 2-d array to 1-d array:'
print mat_2_vec_val
def loop(i, x, p, t):
p_class_t = p[i, t[i]]
return T.dot(
T.flatten(T.grad(p_class_t, x)[i]),
T.flatten(x[i])
)
def build_model(tparams, options, Wemb):
trng = RandomStreams(123)
use_noise = theano.shared(numpy_floatX(0.))
x = T.matrix('x', dtype='int32')
t = T.matrix('t', dtype=config.floatX)
mask = T.matrix('mask', dtype=config.floatX)
y = T.vector('y', dtype='int32')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
x_emb = Wemb[x.flatten()].reshape([n_timesteps,n_samples,options['embDimSize']])
x_t_emb = T.concatenate([t.reshape([n_timesteps,n_samples,1]), x_emb], axis=2) #Adding the time element to the embedding
proj = gru_layer(tparams, x_t_emb, options, mask=mask)
if options['use_dropout']: proj = dropout_layer(proj, use_noise, trng)
p_y_given_x = T.nnet.sigmoid(T.dot(proj, tparams['W_logistic']) + tparams['b_logistic'])
L = -(y * T.flatten(T.log(p_y_given_x)) + (1 - y) * T.flatten(T.log(1 - p_y_given_x)))
cost = T.mean(L)
if options['L2_reg'] > 0.: cost += options['L2_reg'] * (tparams['W_logistic'] ** 2).sum()
return use_noise, x, t, mask, y, p_y_given_x, cost
def unet_crossentropy_loss_sampled(y_true, y_pred):
print 'unet_crossentropy_loss_sampled'
epsilon = 1.0e-4
y_pred_clipped = T.flatten(T.clip(y_pred, epsilon, 1.0-epsilon))
y_true = T.flatten(y_true)
# this seems to work
# it is super ugly though and I am sure there is a better way to do it
# but I am struggling with theano to cooperate
# filter the right indices
indPos = T.nonzero(y_true)[0] # no idea why this is a tuple
indNeg = T.nonzero(1-y_true)[0]
# shuffle
n = indPos.shape[0]
indPos = indPos[srng.permutation(n=n)]
n = indNeg.shape[0]
indNeg = indNeg[srng.permutation(n=n)]
# take equal number of samples depending on which class has less
n_samples = T.cast(T.min([T.sum(y_true), T.sum(1-y_true)]), dtype='int64')
indPos = indPos[:n_samples]
indNeg = indNeg[:n_samples]
loss_vector = -T.mean(T.log(y_pred_clipped[indPos])) - T.mean(T.log(1-y_pred_clipped[indNeg]))
average_loss = T.mean(loss_vector)
print 'average_loss:', average_loss
return average_loss
def apply(self, dataset, can_fit=True):
x = dataset.get_design_matrix()
denseX = T.matrix(dtype=x.dtype)
image_shape = (len(x),) + self.img_shape
X = denseX.reshape(image_shape)
filters = gaussian_filter_9x9().reshape((1,1,9,9))
convout = conv.conv2d(input = X,
filters = filters,
image_shape = image_shape,
filter_shape = (1, 1, 9, 9),
border_mode='full')
# For each pixel, remove mean of 9x9 neighborhood
centered_X = X - convout[:,:,4:-4,4:-4]
# Scale down norm of 9x9 patch if norm is bigger than 1
sum_sqr_XX = conv.conv2d(input = centered_X**2,
filters = filters,
image_shape = image_shape,
filter_shape = (1, 1, 9, 9),
border_mode='full')
denom = T.sqrt(sum_sqr_XX[:,:,4:-4,4:-4])
per_img_mean = T.mean(T.flatten(denom, outdim=3), axis=2)
divisor = T.largest(per_img_mean.dimshuffle((0,1,'x','x')), denom)
new_X = centered_X / divisor
new_X = T.flatten(new_X, outdim=2)
f = theano.function([denseX], new_X)
dataset.set_design_matrix(f(x))
def unet_crossentropy_loss_sampled(y_true, y_pred):
epsilon = 1.0e-4
y_pred_clipped = T.flatten(T.clip(y_pred, epsilon, 1.0-epsilon))
y_true = T.flatten(y_true)
# this seems to work
# it is super ugly though and I am sure there is a better way to do it
# but I am struggling with theano to cooperate
# filter the right indices
classPos = 1
classNeg = 0
indPos = T.eq(y_true, classPos).nonzero()[0]
indNeg = T.eq(y_true, classNeg).nonzero()[0]
#pos = y_true[ indPos ]
#neg = y_true[ indNeg ]
# shuffle
n = indPos.shape[0]
indPos = indPos[UNET.srng.permutation(n=n)]
n = indNeg.shape[0]
indNeg = indNeg[UNET.srng.permutation(n=n)]
# take equal number of samples depending on which class has less
n_samples = T.cast(T.min([ indPos.shape[0], indNeg.shape[0]]), dtype='int64')
#n_samples = T.cast(T.min([T.sum(y_true), T.sum(1-y_true)]), dtype='int64')
indPos = indPos[:n_samples]
indNeg = indNeg[:n_samples]
#loss_vector = -T.mean(T.log(y_pred_clipped[indPos])) - T.mean(T.log(1-y_pred_clipped[indNeg]))
loss_vector = -T.mean(T.log(y_pred_clipped[indPos])) - T.mean(T.log(y_pred_clipped[indNeg]))
loss_vector = T.clip(loss_vector, epsilon, 1.0-epsilon)
average_loss = T.mean(loss_vector)
if T.isnan(average_loss):
average_loss = T.mean( y_pred_clipped[indPos])
return average_loss
def _recurrence(v_h_, x_h_, v_t_, x_t_, a_t_, is_aggressive):
state = tt.concatenate([v_h_, x_h_, tt.flatten(v_t_), tt.flatten(x_t_), tt.flatten(a_t_)])
h0 = tt.dot(state, self.W_a_0) + self.b_a_0
relu0 = tt.nnet.relu(h0)
h1 = tt.dot(relu0, self.W_a_1) + self.b_a_1
relu1 = tt.nnet.relu(h1)
h2 = tt.dot(relu1, self.W_a_2) + self.b_a_2
relu2 = tt.nnet.relu(h2)
a = tt.dot(relu2, self.W_a_c)
v_h, x_h, v_t, x_t, a_t, cost_transition = _step_state(v_h_, x_h_, v_t_, x_t_, a_t_, a, is_aggressive)
# cost:
# 0. smooth acceleration policy
cost_accel = tt.abs_(a)
# 1. forcing the host to move forward (until the top point of the roundabout)
cost_progress = tt.nnet.relu(0.5*self.two_pi_r-x_h)
# 2. keeping distance from close vehicles
x_abs_diffs = tt.abs_(x_h - x_t)
cost_accident = tt.mean(3*tt.nnet.relu( self.require_distance-x_abs_diffs )) * (x_h > - 0.5*self.host_length) #tt.nnet.sigmoid(x_h + 0.5*self.host_length)
cost = self.alpha_accel * cost_accel + self.alpha_progress * cost_progress + self.alpha_accident * cost_accident
return (v_h, x_h, v_t, x_t, a_t, cost, cost_transition), t.scan_module.until(x_h[0]>=0.45*self.two_pi_r)
def __call__(self, x, leak):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
if leak.ndim == 1:
return T.flatten(f1, 1)[0] * x + T.flatten(f2, 1)[0] * abs(x)
else:
return f1 * x + f2 * abs(x)
def __create_node_set(self, n_features, n_output, data_in, note_set_name, weightsFunc = None,):
prev_out = data_in
prev_dim = n_features
layers = []
n_weights = 0
weights_list = []
state = None
for i_h_layer in range(0,len(self.hidden_dimensions)):
n_hidden_nodes = self.hidden_dimensions[i_h_layer]
weights = None
#weights = np.ones((prev_dim,n_hidden_nodes)) - 0.5
bias = None
if weightsFunc is not None:
weights,bias,state = weightsFunc(i_h_layer,state)
#acutal hidden layer
hidden_layer = Layer(data_in=prev_out,
n_input=prev_dim,
n_output=n_hidden_nodes,
link_function=self.link_function_hidden,
weights=weights,
bias=bias,
name=note_set_name + " Hidden Layer")
weights_list.append(hidden_layer.weights)
weights_list.append(hidden_layer.bias)
layers.append(hidden_layer)
n_weights += (prev_dim+1)*n_hidden_nodes
prev_out = hidden_layer.output
prev_dim = n_hidden_nodes
weights = None
#weights = np.ones((prev_dim,n_output)) - 0.5
bias = None
if weightsFunc is not None:
weights,bias,state = weightsFunc(len(self.hidden_dimensions),state)
output_layer = Layer(
data_in=prev_out,
n_input=prev_dim,
n_output=n_output,
link_function=self.link_function_output,
weights=weights,
bias=bias,
name=note_set_name + " Output Layer")
weights_list.append(output_layer.weights)
weights_list.append(output_layer.bias)
layers.append(output_layer)
n_weights += (prev_dim+1)*n_output
#concatenate weights into one huge vector
flat_weights = T.concatenate([T.flatten(item) for item in weights_list])
flat_weights.name = "Network " + note_set_name + " Weights"
#compute MSE
y = self.__y
errors = y - output_layer.output
mse = T.mean(T.sqr(errors))
normalized_mse = mse / 2.0
normalized_mse.name = note_set_name + " MSE"
grads = T.concatenate([T.flatten(item) for item in T.grad(normalized_mse, weights_list)])
grads.name = note_set_name + " Gradients"
return layers,grads,normalized_mse,weights_list, n_weights, flat_weights
def model(X1, X2, w1, w2, w3, p_drop_conv):
# first half of the first layer
l1a = T.flatten(dropout(T.mean(rectify(conv2d(X1, w1, border_mode='valid')), axis=3), p_drop_conv), outdim=2)
# second half of the first layer
l1b = T.flatten(dropout(T.mean(rectify(conv2d(X2, w2, border_mode='valid')), axis=3), p_drop_conv), outdim=2)
# combine two pars as first layer
l1 = T.concatenate([l1a, l1b], axis=1)
# combine two pars as first layer
pyx = T.dot(l1, w3)
return pyx
def lower_bound(self):
mu = T.flatten(self.trunc_output, outdim=2)
inp = T.flatten(self.inpt, outdim=2)
if self.out_distribution == True:
sigma = T.mean(T.flatten(self.trunk_sigma, outdim=2))
else:
sigma = 0
# log_gauss = 0.5*np.log(2 * np.pi) + 0.5*sigma + 0.5 * ((inp - mu) / T.exp(sigma))**2.
log_gauss = T.sum(0.5 * np.log(2 * np.pi) + 0.5 * sigma + 0.5 * ((inp - mu) / T.exp(sigma)) ** 2.0, axis=1)
return T.mean(log_gauss - self.latent_layer.prior)
def t_unroll_ae(wts, bs, tied_wts=False):
''' Flattens matrices and concatenates to a vector - specifically for autoencoders '''
# if we have tied weights, this vector will be comprised of a single matrix and two
# distinct bias vectors
if tied_wts:
v = np.array([], type=theano.config.floatX)
v = T.concatenate(
(v, T.flatten(wts[0]), T.flatten(bs[0]), T.flatten(bs[1])))
return v
return t_unroll(wts, bs)
def model(X,
h2_u, h3_u,
h2_s, h3_s,
w, w2, g2, b2, w3, g3, b3, wy
):
h = lrelu(dnn_conv(X, w, subsample=(2, 2), border_mode=(2, 2)))
h2 = lrelu(batchnorm(dnn_conv(h, w2, subsample=(2, 2), border_mode=(2, 2)), g=g2, b=b2, u=h2_u, s=h2_s))
h3 = lrelu(batchnorm(dnn_conv(h2, w3, subsample=(2, 2), border_mode=(2, 2)), g=g3, b=b3, u=h3_u, s=h3_s))
h = T.flatten(dnn_pool(h, (4, 4), (4, 4), mode='max'), 2)
h2 = T.flatten(dnn_pool(h2, (2, 2), (2, 2), mode='max'), 2)
h3 = T.flatten(dnn_pool(h3, (1, 1), (1, 1), mode='max'), 2)
f = T.concatenate([h, h2, h3], axis=1)
return [f]
def model(X, w1, w2, w3, p_drop_conv, p_drop_hidden):
l1a = rectify(conv2d(X, w1, border_mode='full'))
l1 = max_pool_2d(l1a, (2, 2))
l1 = dropout(l1, p_drop_conv)
dropout(T.flatten(max_pool_2d(rectify(conv2d(X, w2)), (2,2)), outdim=2), 0.3)
l2a = rectify(conv2d(l1, w2))
l2b = max_pool_2d(l2a, (2, 2))
l2 = T.flatten(l2b, outdim=2)
l2 = dropout(l2, p_drop_conv)
pyx = softmax(T.dot(l2, w3))
return l1, l2, pyx
def set_sampling_function(decoder_feature_function,
decoder_red_function,
decoder_green_function,
decoder_blue_function):
hidden_data = T.matrix(name='hidden_data',
dtype=theano.config.floatX)
# decoder
decoder_outputs = decoder_feature_function(hidden_data)
decoder_feature = decoder_outputs[1]
decoder_red = decoder_red_function(decoder_feature)
decoder_green = decoder_green_function(decoder_feature)
decoder_blue = decoder_blue_function(decoder_feature)
num_samples = decoder_red.shape[0]
num_rows = decoder_red.shape[2]
num_cols = decoder_red.shape[3]
num_pixels = num_rows*num_cols
# shape = (num_samples, num_intensity, num_pixels)
decoder_red = T.flatten(decoder_red, 3)
decoder_green = T.flatten(decoder_green, 3)
decoder_blue = T.flatten(decoder_blue, 3)
# shape = (num_samples, num_pixels, num_intensity)
decoder_red = T.swapaxes(decoder_red, axis1=1, axis2=2)
decoder_green = T.swapaxes(decoder_green, axis1=1, axis2=2)
decoder_blue = T.swapaxes(decoder_blue, axis1=1, axis2=2)
# shape = (num_samples*num_pixels, num_intensity)
decoder_red = decoder_red.reshape((num_samples*num_pixels, -1))
decoder_green = decoder_green.reshape((num_samples*num_pixels, -1))
decoder_blue = decoder_blue.reshape((num_samples*num_pixels, -1))
# softmax
decoder_red = T.argmax(T.nnet.softmax(decoder_red),axis=1)
decoder_green = T.argmax(T.nnet.softmax(decoder_green),axis=1)
decoder_blue = T.argmax(T.nnet.softmax(decoder_blue),axis=1)
decoder_red = decoder_red.reshape((num_samples, 1, num_rows, num_cols))
decoder_green = decoder_green.reshape((num_samples, 1, num_rows, num_cols))
decoder_blue = decoder_blue.reshape((num_samples, 1, num_rows, num_cols))
decoder_image = T.concatenate([decoder_red, decoder_green, decoder_blue], axis=1)
function_inputs = [hidden_data,]
function_outputs = [decoder_image,]
function = theano.function(inputs=function_inputs,
outputs=function_outputs,
on_unused_input='ignore')
return function
def gauss_style_loss(x_truth, x_guess, log_var=0., scale=1., use_huber=False):
# compute gram matrices for the two batches of convolutional features
g_t = T.flatten(gram_matrix(x_truth), 2)
g_g = T.flatten(gram_matrix(x_guess), 2)
# get normalization factors based on the size of feature maps
# N = T.cast(x_truth.shape[1], 'floatX')
# M = T.cast(x_truth.shape[2] * x_truth.shape[3], 'floatX')
# compute a pseudo-Gaussian loss on difference between gram matrices
loss = log_prob_gaussian(g_t, g_g, log_vars=log_var, do_sum=False,
use_huber=use_huber, mask=None)
# take sum over gram matrix entries and normalize for feature map size
# loss = (scale / (N**2. * M)) * T.sum(loss, axis=1, keepdims=False)
loss = T.sum(loss, axis=1, keepdims=False)
return loss
def L2SVMcost(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
'''p = -T.ones_like((y.shape[0],7))
result, updates = theano.scan(fn = lambda p,y: T.basic.set_subtensor(p[i,y[i]]=1),
outputs_info = -T.ones_like((y.shape[0],7)),
non_sequences = y,
n_steps = y.shape[0])
final_result = result[-1]
f = theano.function([y,p],final_result,updates = updates)
for i in xrange(500):
p = T.basic.set_subtensor(p[i,y[i]]=1)
print p.shape
print f(y,p)
print f(y,p).shape'''
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
z = 0.5*T.dot( T.flatten(self.W,outdim=1), T.flatten(self.W, outdim=1)) + 0.5*T.dot( T.flatten(self.b,outdim=1), T.flatten(self.b, outdim=1)) +0.6* T.sum(T.maximum(0,(1-self.p_y_given_x *y)),axis=1).mean()
#zk = theano.tensor.scalar('zk')
#zp = theano.printing.Print('this is a very important value')(zk)
#f = theano.function([zk],zp)
#z = theano.shared(z)
#f(z)
return z
def create_model():
"""Create the deep autoencoder model with Blocks, and load MNIST."""
mlp = MLP(activations=[Logistic(), Logistic(), Logistic(), None,
Logistic(), Logistic(), Logistic(), Logistic()],
dims=[784, 1000, 500, 250, 30, 250, 500, 1000, 784],
weights_init=Sparse(15, IsotropicGaussian()),
biases_init=Constant(0))
mlp.initialize()
x = tensor.matrix('features')
x_hat = mlp.apply(tensor.flatten(x, outdim=2))
squared_err = SquaredError().apply(tensor.flatten(x, outdim=2), x_hat)
cost = BinaryCrossEntropy().apply(tensor.flatten(x, outdim=2), x_hat)
return x, cost, squared_err
def set_updater_function(feature_extractor,
sample_generator,
generator_parameters,
generator_optimizer):
# set input data, hidden data
input_data = T.tensor4(name='input_data',
dtype=theano.config.floatX)
hidden_data = T.matrix(name='hidden_data',
dtype=theano.config.floatX)
# extract feature from input data
positive_features = feature_extractor(input_data)
# sample data
negative_features = sample_generator(hidden_data)
negative_data = negative_features[-1]
negative_features = negative_features[:-1]
# moment matching
moment_match_cost = 0
for i in xrange(len(positive_features)):
pos_feat = positive_features[i]
neg_feat = negative_features[i]
moment_match_cost += T.mean(T.sqr(T.mean(pos_feat, axis=0)-T.mean(neg_feat, axis=0)))
moment_match_cost += T.mean(T.sqr(T.mean(T.sqr(pos_feat), axis=0)-T.mean(T.sqr(neg_feat), axis=0)))
pos_feat = T.flatten(input_data, 2)
neg_feat = T.flatten(negative_data, 2)
moment_match_cost += T.mean(T.sqr(T.mean(pos_feat, axis=0)-T.mean(neg_feat, axis=0)))
moment_match_cost += T.mean(T.sqr(T.mean(T.sqr(pos_feat), axis=0)-T.mean(T.sqr(neg_feat), axis=0)))
generator_updates = generator_optimizer(generator_parameters,
moment_match_cost)
# updater function input
updater_function_inputs = [input_data,
hidden_data]
# updater function output
updater_function_outputs = [moment_match_cost,
negative_data]
# updater function
updater_function = theano.function(inputs=updater_function_inputs,
outputs=updater_function_outputs,
updates=generator_updates,
on_unused_input='ignore')
return updater_function
def jacobian_mul_vector_l_flat(y, x, W, v, x_val, W_val, v_val):
J = theano.gradient.jacobian(y, x)
J_flat = T.flatten(J, J.ndim - 1) # The jacobian result on flattened matrix x
VJ = v.dot(J_flat)
VJ_reshape = T.reshape(VJ, T.shape(x))
f_VJ = theano.function([x, W, v], VJ_reshape)
return f_VJ(x_val, W_val, v_val)
def apply(self, dataset, can_fit=True):
x = dataset.get_design_matrix()
denseX = T.matrix(dtype=x.dtype)
image_shape = (len(x),) + self.img_shape
X = denseX.reshape(image_shape)
ones_patch = T.ones((1,1,9,9), dtype=x.dtype)
convout = conv.conv2d(input = X,
filters = ones_patch / (9.*9.),
image_shape = image_shape,
filter_shape = (1, 1, 9, 9),
border_mode='full')
# For each pixel, remove mean of 3x3 neighborhood
centered_X = X - convout[:,:,4:-4,4:-4]
# Scale down norm of 3x3 patch if norm is bigger than 1
sum_sqr_XX = conv.conv2d(input = centered_X**2,
filters = ones_patch,
image_shape = image_shape,
filter_shape = (1, 1, 9, 9),
border_mode='full')
denom = T.sqrt(sum_sqr_XX[:,:,4:-4,4:-4])
xdenom = denom.reshape(X.shape)
new_X = centered_X / T.largest(1.0, xdenom)
new_X = T.flatten(new_X, outdim=2)
f = theano.function([denseX], new_X)
dataset.set_design_matrix(f(x))
def model(X, w1, w2, w3, Max_Pooling_Shape, p_drop_conv, p_drop_hidden):
l1 = T.flatten(
dropout(max_pool_2d(rectify(conv2d(X, w1, border_mode="valid")), Max_Pooling_Shape), p_drop_conv), outdim=2
)
l2 = dropout(rectify(T.dot(l1, w2)), p_drop_hidden)
pyx = softmax(T.dot(l2, w3))
return pyx
def model_conv(
X,
w_1,
w_2,
w_3,
w_h2,
w_o,
p_use_input,
p_use_hidden
):
X = dropout(X, p_use_input)
# first convolutional layer:
conv_layer_1 = rectify( T.nnet.conv2d(X, w_1, border_mode = 'full' ))
sub_layer_1 = T.signal.downsample.max_pool_2d(conv_layer_1, (2, 2) )
out_1 = dropout(sub_layer_1, p_use_input)
# second convolutional layer:
conv_layer_2 = rectify( T.nnet.conv2d(out_1, w_2) )
sub_layer_2 = T.signal.downsample.max_pool_2d(conv_layer_2, (2, 2) )
out_2 = dropout(sub_layer_2, p_use_hidden)
# third convolutional layer:
conv_layer_3 = rectify( T.nnet.conv2d(out_2, w_3) )
sub_layer_3 = T.signal.downsample.max_pool_2d(conv_layer_3, (2, 2) )
out_3 = dropout(sub_layer_3, p_use_hidden)
out_3 = T.flatten(out_3, outdim = 2)
h2 = rectify(T.dot(out_3, w_h2))
h2 = dropout(h2, p_use_hidden)
# output layer, activation function = softmax
py_x = softmax(T.dot(h2, w_o))
return out_1, out_2, out_3, h2, py_x
def test_log1msigm_to_softplus(self):
x = T.matrix()
out = T.log(1 - sigmoid(x))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
assert isinstance(topo[0].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[1].op.scalar_op, theano.scalar.Neg)
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a flatten
out = T.log(1 - T.flatten(sigmoid(x)))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert isinstance(topo[0].op, T.Flatten)
assert isinstance(topo[1].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[2].op.scalar_op, theano.scalar.Neg)
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a reshape
out = T.log(1 - sigmoid(x).reshape([x.size]))
f = theano.function([x], out, mode=self.m)
topo = f.maker.fgraph.toposort()
#assert len(topo) == 3
assert any(isinstance(node.op, T.Reshape) for node in topo)
assert any(isinstance(getattr(node.op, 'scalar_op', None),
theano.tensor.nnet.sigm.ScalarSoftplus)
for node in topo)
f(numpy.random.rand(54, 11).astype(config.floatX))
def _flatten_1d_or_2d(v):
if v.ndim > 2:
return T.flatten(v, outdim=2)
elif 1 <= v.ndim <= 2:
return v
else:
raise ValueError
def build_objective(model, deterministic=False, epsilon=1e-12):
predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
targets = T.cast(T.flatten(nn.layers.get_output(model.l_target)), 'int32')
p = predictions[T.arange(predictions.shape[0]), targets]
p = T.clip(p, epsilon, 1.)
loss = T.mean(T.log(p))
return -loss
def feature_extractor(input_data):
# conv stage 0 (64x64=>32x32)
h0_0 = dnn_conv(input_data, conv_w0_0, border_mode=(1, 1)) + conv_b0_0.dimshuffle("x", 0, "x", "x")
h0_1 = dnn_conv(relu(h0_0), conv_w0_1, border_mode=(1, 1)) + conv_b0_1.dimshuffle("x", 0, "x", "x")
h0 = dnn_pool(relu(h0_1), ws=(2, 2), stride=(2, 2))
# conv stage 1 (32x32=>16x16)
h1_0 = dnn_conv(h0, conv_w1_0, border_mode=(1, 1)) + conv_b1_0.dimshuffle("x", 0, "x", "x")
h1_1 = dnn_conv(relu(h1_0), conv_w1_1, border_mode=(1, 1)) + conv_b1_1.dimshuffle("x", 0, "x", "x")
h1 = dnn_pool(relu(h1_1), ws=(2, 2), stride=(2, 2))
# conv stage 2 (16x16=>8x8)
h2_0 = dnn_conv(h1, conv_w2_0, border_mode=(1, 1)) + conv_b2_0.dimshuffle("x", 0, "x", "x")
h2_1 = dnn_conv(relu(h2_0), conv_w2_1, border_mode=(1, 1)) + conv_b2_1.dimshuffle("x", 0, "x", "x")
h2_2 = dnn_conv(relu(h2_1), conv_w2_2, border_mode=(1, 1)) + conv_b2_2.dimshuffle("x", 0, "x", "x")
h2 = dnn_pool(relu(h2_2), ws=(2, 2), stride=(2, 2))
# conv stage 3 (8x8=>4x4)
h3_0 = dnn_conv(h2, conv_w3_0, border_mode=(1, 1)) + conv_b3_0.dimshuffle("x", 0, "x", "x")
h3_1 = dnn_conv(relu(h3_0), conv_w3_1, border_mode=(1, 1)) + conv_b3_1.dimshuffle("x", 0, "x", "x")
h3_2 = dnn_conv(relu(h3_1), conv_w3_2, border_mode=(1, 1)) + conv_b3_2.dimshuffle("x", 0, "x", "x")
h3 = dnn_pool(relu(h3_2), ws=(2, 2), stride=(2, 2))
# conv stage 4 (4x4=>2x2)
h4_0 = dnn_conv(h3, conv_w4_0, border_mode=(1, 1)) + conv_b4_0.dimshuffle("x", 0, "x", "x")
h4_1 = dnn_conv(relu(h4_0), conv_w4_1, border_mode=(1, 1)) + conv_b4_1.dimshuffle("x", 0, "x", "x")
h4_2 = dnn_conv(relu(h4_1), conv_w4_2, border_mode=(1, 1)) + conv_b4_2.dimshuffle("x", 0, "x", "x")
h4 = dnn_pool(relu(h4_2), ws=(2, 2), stride=(2, 2))
return T.flatten(h4, 2)
def convolutional_model(X, w_1, w_2, w_3, w_4, w_5, w_6, p_1, p_2, p_3, p_4, p_5):
l1 = dropout(T.tanh( max_pool_2d(T.maximum(conv2d(X, w_1, border_mode='full'),0.), (2, 2),ignore_border=True) + b_1.dimshuffle('x', 0, 'x', 'x') ), p_1)
l2 = dropout(T.tanh( max_pool_2d(T.maximum(conv2d(l1, w_2), 0.), (2, 2),ignore_border=True) + b_2.dimshuffle('x', 0, 'x', 'x') ), p_2)
l3 = dropout(T.flatten(T.tanh( max_pool_2d(T.maximum(conv2d(l2, w_3), 0.), (2, 2),ignore_border=True) + b_3.dimshuffle('x', 0, 'x', 'x') ), outdim=2), p_3)# flatten to switch back to 1d layers
l4 = dropout(T.maximum(T.dot(l3, w_4), 0.), p_4)
l5 = dropout(T.maximum(T.dot(l4, w_5), 0.), p_5)
return T.dot(l5, w_6)
def lp_norm(self, n, k, r, c, z):
'''
Lp = ( 1/n * sum(|x_i|^p, 1..n))^(1/p) where p = 1 + ln(1+e^P)
:param n:
:param k:
:param r:
:param c:
:param z:
:return:
'''
ds0, ds1 = self.pool_size
st0, st1 = self.stride
pad_h = self.pad[0]
pad_w = self.pad[1]
row_st = r * st0
row_end = T.minimum(row_st + ds0, self.img_rows)
row_st = T.maximum(row_st, self.pad[0])
row_end = T.minimum(row_end, self.x_m2d + pad_h)
col_st = c * st1
col_end = T.minimum(col_st + ds1, self.img_cols)
col_st = T.maximum(col_st, self.pad[1])
col_end = T.minimum(col_end, self.x_m1d + pad_w)
Lp = T.pow(
T.mean(T.pow(
T.abs_(T.flatten(self.y[n, k, row_st:row_end, col_st:col_end], 1)),
1 + T.log(1 + T.exp(self.P))
)),
1 / (1 + T.log(1 + T.exp(self.P)))
)
return T.set_subtensor(z[n, k, r, c], Lp)
def model(X, params, featMaps, pieces, pDropConv, pDropHidden):
lnum = 0 # conv: (32, 32) pool: (16, 16)
layer = conv2d(X, params[lnum][0], border_mode='half') + \
params[lnum][1].dimshuffle('x', 0, 'x', 'x')
layer = maxout(layer, featMaps[lnum], pieces[lnum])
layer = pool_2d(layer, (2, 2), st=(2, 2), ignore_border=False, mode='max')
layer = basicUtils.dropout(layer, pDropConv)
lnum += 1 # conv: (16, 16) pool: (8, 8)
layer = conv2d(layer, params[lnum][0], border_mode='half') + \
params[lnum][1].dimshuffle('x', 0, 'x', 'x')
layer = maxout(layer, featMaps[lnum], pieces[lnum])
layer = pool_2d(layer, (2, 2), st=(2, 2), ignore_border=False, mode='max')
layer = basicUtils.dropout(layer, pDropConv)
lnum += 1 # conv: (8, 8) pool: (4, 4)
layer = conv2d(layer, params[lnum][0], border_mode='half') + \
params[lnum][1].dimshuffle('x', 0, 'x', 'x')
layer = maxout(layer, featMaps[lnum], pieces[lnum])
layer = pool_2d(layer, (2, 2), st=(2, 2), ignore_border=False, mode='max')
layer = basicUtils.dropout(layer, pDropConv)
lnum += 1
layer = T.flatten(layer, outdim=2)
layer = T.dot(layer, params[lnum][0]) + params[lnum][1].dimshuffle('x', 0)
layer = relu(layer, alpha=0)
layer = basicUtils.dropout(layer, pDropHidden)
lnum += 1
layer = T.dot(layer, params[lnum][0]) + params[lnum][1].dimshuffle('x', 0)
layer = relu(layer, alpha=0)
layer = basicUtils.dropout(layer, pDropHidden)
lnum += 1
return softmax(T.dot(layer, params[lnum][0]) + params[lnum][1].dimshuffle('x', 0)) # 如果使用nnet中的softmax训练产生NAN
input_logits = T.ivector('inputs')
input_logits.tag.test_value = bk.logits("abbabaabba", input_logits.dtype)
xs = T.extra_ops.to_one_hot(input_logits, len(bk.character_set))
target_logits = T.ivector('targets')
target_logits.tag.test_value = bk.logits("bbabaabbab", target_logits.dtype)
outputs, read_address, memory = (partial_bptt(xs, read_address_with_grads,
memory_with_grads, w, output_w))
bptt_cost = softmax_log_likelihood(outputs, target_logits)
if l2_param > 0: bptt_cost += l2_param * T.sum((w**2) / 2) # L2 regularization
j_read_address_w = T.jacobian(read_address, w)
j_memory_w = T.reshape(T.jacobian(T.flatten(memory), w),
prev_memory.get_value().shape + w.get_value().shape)
# Reshape will make things broadcastable by default, but then updating fails
# because shared variables are not broadcastable by default and broadcastable
# has to match for a shared var update. We don't want to boradcast anyway.
# This is only a problem if the memory depth is 1, so make the depth not BCable
j_memory_w = T.unbroadcast(j_memory_w, prev_memory.ndim - 1)
update_a_grad = saved_a_grad, j_read_address_w
update_m_grad = saved_m_grad, j_memory_w
update_address = prev_read_address, read_address
update_memory = prev_memory, memory
weight_updates = list(
lasagne.updates.adadelta(bptt_cost, [w, output_w]).items())
def _recurrence(time_step, x_h_, v_h_, angle_, speed_, t_h_, x_t_,
v_t_, a_t_, t_t_, exist, is_leader, x_goal, turn_vec_h,
turn_vec_t):
# state
'''
1. host
1.1 position (2) - (x,y) coordinates in cross coordinate system
1.2 speed (2) - (v_x,v_y)
# 1.3 acceleration (2) - (a_x,a_y)
# 1.4 waiting time (1) - start counting on full stop. stop counting when clearing the junction
1.5 x_goal (2) - destination position (indicates different turns)
total = 5
2. right lane car
2.1 position (2) - null value = (-1,-1)
2.2 speed (2) - null value = (0,0)
2.3 acceleration (2) - null value = (0,0)
2.4 waiting time (1) - null value = 0
total = 7
3. front lane car
3.1 position (2)
3.2 speed (2)
3.3 acceleration (2)
3.4 waiting time (1)
total = 7
4. target 3
4.1 position (2)
4.2 speed (2)
4.3 acceleration (2)
4.4 waiting time (1)
total = 7
total = 26
'''
# host_state_vec = tt.concatenate([x_h_, v_h_, t_h_])
ang_spd = tt.stack([angle_, speed_])
host_state_vec = tt.concatenate([x_h_, ang_spd, x_goal])
# target_state_vec = tt.concatenate([tt.flatten(x_t_), tt.flatten(v_t_), tt.flatten(a_t_), tt.flatten(t_t_)])
target_state_vec = tt.concatenate([
tt.flatten(x_t_),
tt.flatten(v_t_),
tt.flatten(a_t_), is_leader
])
state = tt.concatenate([host_state_vec, target_state_vec])
h0 = tt.dot(state, self.W_0) + self.b_0
relu0 = tt.nnet.relu(h0)
h1 = tt.dot(relu0, self.W_1) + self.b_1
relu1 = tt.nnet.relu(h1)
h2 = tt.dot(relu1, self.W_2) + self.b_2
relu2 = tt.nnet.relu(h2)
a_h = tt.dot(relu2, self.W_c)
x_h, v_h, angle, speed, t_h, x_t, v_t, a_t, t_t = _step_state(
x_h_, v_h_, angle_, speed_, t_h_, turn_vec_h, x_t_, v_t_, t_t_,
turn_vec_t, a_h, exist, time_step)
# cost:
discount_factor = 0.99**time_step
# 0. smooth driving policy
cost_steer = discount_factor * a_h[0]**2
cost_accel = discount_factor * a_h[1]**2
# 1. forcing the host to move forward
dist_from_goal = tt.mean((x_goal - x_h)**2)
cost_progress = discount_factor * dist_from_goal
# 2. keeping distance from in front vehicles
d_t_h = x_t - x_h
h_t_dists = (d_t_h**2).sum(axis=1)
# v_h_norm = tt.sqrt((v_h**2).sum())
# d_t_h_norm = tt.sqrt((d_t_h**2).sum(axis=1))
#
# denominator = v_h_norm * d_t_h_norm
#
# host_targets_orientation = tt.dot(d_t_h, v_h) / (denominator + 1e-3)
#
# in_fornt_targets = tt.nnet.sigmoid(5 * host_targets_orientation)
#
# close_targets = tt.sum(tt.abs_(d_t_h))
#
# cost_accident = tt.mean(in_fornt_targets * close_targets)
cost_accident = tt.sum(
tt.nnet.relu(self.require_distance - h_t_dists))
# 3. rail divergence
cost_right_rail = _dist_from_rail(
x_h, self.right_rail_center,
self.right_rail_radius) * turn_vec_h[0]
cost_front_rail = (x_h[0] - self.lw / 2)**2 * turn_vec_h[1]
cost_left_rail = _dist_from_rail(
x_h, self.left_rail_center,
self.left_rail_radius) * turn_vec_h[2]
cost_rail = cost_right_rail + cost_left_rail + cost_front_rail
return (x_h, v_h, angle, speed, t_h, x_t, v_t, a_t, t_t,
cost_steer, cost_accel, cost_progress, cost_accident,
cost_rail,
a_h), t.scan_module.until(dist_from_goal < 0.001)
def set_network_trainer(input_data,
input_mask,
target_data,
target_mask,
num_outputs,
network,
updater,
learning_rate,
grad_max_norm=10.,
l2_lambda=1e-5,
load_updater_params=None):
# get one hot target
one_hot_target_data = T.extra_ops.to_one_hot(y=T.flatten(target_data, 1),
nb_class=num_outputs,
dtype=floatX)
# get network output data
predict_data = get_output(network, deterministic=False)
num_seqs = predict_data.shape[0]
# get prediction cost
predict_data = T.reshape(x=predict_data,
newshape=(-1, num_outputs),
ndim=2)
predict_data = predict_data - T.max(predict_data, axis=-1, keepdims=True)
predict_data = predict_data - T.log(T.sum(T.exp(predict_data), axis=-1, keepdims=True))
train_predict_cost = -T.sum(T.mul(one_hot_target_data, predict_data), axis=-1)
train_predict_cost = train_predict_cost*T.flatten(target_mask, 1)
train_model_cost = train_predict_cost.sum()/num_seqs
train_frame_cost = train_predict_cost.sum()/target_mask.sum()
# get regularizer cost
train_regularizer_cost = regularize_network_params(network, penalty=l2)*l2_lambda
# get network parameters
network_params = get_all_params(network, trainable=True)
# get network gradients
network_grads = theano.grad(cost=train_model_cost + train_regularizer_cost,
wrt=network_params)
if grad_max_norm>0.:
network_grads, network_grads_norm = total_norm_constraint(tensor_vars=network_grads,
max_norm=grad_max_norm,
return_norm=True)
else:
network_grads_norm = T.sqrt(sum(T.sum(grad**2) for grad in network_grads))
# set updater
train_lr = theano.shared(lasagne.utils.floatX(learning_rate))
train_updates, trainer_params = updater(loss_or_grads=network_grads,
params=network_params,
learning_rate=train_lr,
load_params_dict=load_updater_params)
# get training (update) function
training_fn = theano.function(inputs=[input_data,
input_mask,
target_data,
target_mask],
outputs=[train_frame_cost,
network_grads_norm],
updates=train_updates)
return training_fn, trainer_params
def Hx_plain():
Hx_plain_splits = TT.grad(TT.sum(
[TT.sum(g * x) for g, x in zip(constraint_grads, xs)]),
wrt=params,
disconnected_inputs='warn')
return TT.concatenate([TT.flatten(s) for s in Hx_plain_splits])
def get_l2_regularization(self, extra_params=[]):
return T.mean(
T.concatenate([T.flatten(layer.W)
for layer in self.layers] + extra_params)**2.)
def step(input_n, cell_previous, hid_previous, *args):
# word-by-word attention
mh = T.dot(input_n, self.W_h_attend) + T.dot(
hid_previous, self.W_m_attend)
# mh is (n_batch, 1, n_features)
mh = mh.dimshuffle(0, 'x', 1)
M = T.dot(encoder_hs, self.W_y_attend) + mh
# (n_batch, n_time_steps, n_features)
M = nonlinearities.tanh(M)
# alpha is (n_batch, n_time_steps, 1)
alpha = T.dot(M, self.w_attend)
# now is (n_batch, n_time_steps)
alpha = T.flatten(alpha, 2)
# 0 after softmax is not 0, f**k, my mistake.
# when i > encoder_seq_len, fill alpha_i to -np.inf
# alpha = T.switch(encoder_mask, alpha, -np.inf)
alpha = T.nnet.softmax(alpha)
# apply encoder_mask to alpha
# encoder_mask is (n_batch, n_time_steps)
# when i > encoder_seq_len, alpha_i should be 0.
# actually not need mask, but in case of error
# alpha = alpha * encoder_mask
alpha = alpha.dimshuffle(0, 1, 'x')
weighted_encoder = T.sum(encoder_hs * alpha, axis=1)
r = weighted_encoder
# (n_batch, n_features)
input_n = T.concatenate([r, input_n], axis=1)
if not self.precompute_input:
input_n = T.dot(input_n, W_in_stacked) + b_stacked
# Calculate gates pre-activations and slice
gates = input_n + T.dot(hid_previous, W_hid_stacked)
# Clip gradients
if self.grad_clipping:
gates = theano.gradient.grad_clip(gates, -self.grad_clipping,
self.grad_clipping)
# Extract the pre-activation gate values
ingate = slice_w(gates, 0)
forgetgate = slice_w(gates, 1)
cell_input = slice_w(gates, 2)
outgate = slice_w(gates, 3)
if self.peepholes:
# Compute peephole connections
ingate += cell_previous * self.W_cell_to_ingate
forgetgate += cell_previous * self.W_cell_to_forgetgate
# Apply nonlinearities
ingate = self.nonlinearity_ingate(ingate)
forgetgate = self.nonlinearity_forgetgate(forgetgate)
cell_input = self.nonlinearity_cell(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
if self.peepholes:
outgate += cell * self.W_cell_to_outgate
outgate = self.nonlinearity_outgate(outgate)
# Compute new hidden unit activation
hid = outgate * self.nonlinearity(cell)
return [cell, hid]
def forward(self, inputtensor):
inputimage = inputtensor[0]
return (T.flatten(inputimage, outdim=2),)
def MSE(self):
#self.cost = T.mean(T.sum((self.y-self.fully_connected.output)**2))
m = T.sum(T.flatten(
(self.inpt - self.trunc_output)**2, outdim=2) * self.df,
axis=1)
return T.mean(4 * m - self.latent_layer.prior)
def __init__(self, dim_z, x_train, x_test, diff=None, magic=5000):
####################################### SETTINGS ###################################
self.x_train = x_train
self.x_test = x_test
self.diff = diff
self.batch_size = 100.
self.learning_rate = theano.shared(np.float32(0.0008))
self.momentum = 0.3
self.performance = {"train": [], "test": []}
self.inpt = T.ftensor4(name='input')
self.df = T.fmatrix(name='differential')
self.dim_z = dim_z
self.generative_z = theano.shared(np.float32(np.zeros([1, dim_z])))
self.activation = relu
self.generative = False
self.out_distribution = False
#self.y = T.matrix(name="y")
self.in_filters = [64, 64, 64]
self.filter_lengths = [10., 10., 10.]
self.params = []
#magic = 73888.
self.magic = magic
self.dropout_symbolic = T.fscalar()
self.dropout_prob = theano.shared(np.float32(0.0))
####################################### LAYERS ######################################
# LAYER 1 ##############################
self.conv1 = one_d_conv_layer(self.inpt,
self.in_filters[0],
1,
self.filter_lengths[0],
param_names=["W1", 'b1'])
self.params += self.conv1.params
self.bn1 = batchnorm(self.conv1.output)
self.nl1 = self.activation(self.bn1.X)
self.maxpool1 = ds.max_pool_2d(self.nl1, [3, 1],
st=[2, 1],
ignore_border=False).astype(
theano.config.floatX)
self.layer1_out = dropout(self.maxpool1, self.dropout_symbolic)
#self.layer1_out = self.maxpool1
# LAYER2 ################################
self.flattened = T.flatten(self.layer1_out, outdim=2)
# Variational Layer #####################
self.latent_layer = variational_gauss_layer(self.flattened, self.magic,
dim_z)
self.params += self.latent_layer.params
self.latent_out = self.latent_layer.output
# Hidden Layer #########################
self.hidden_layer = hidden_layer(self.latent_out, dim_z, self.magic)
self.params += self.hidden_layer.params
self.hid_out = dropout(
self.activation(self.hidden_layer.output).reshape(
(self.inpt.shape[0], self.in_filters[-1],
int(self.magic / self.in_filters[-1]), 1)),
self.dropout_symbolic)
# Devonvolutional 1 ######################
self.deconv1 = one_d_deconv_layer(self.hid_out,
1,
self.in_filters[2],
self.filter_lengths[2],
pool=2.,
param_names=["W3", 'b3'],
distribution=False)
self.params += self.deconv1.params
#self.nl_deconv1 = dropout(self.activation(self.deconv1.output),self.dropout_symbolic)
self.tanh_out = self.deconv1.output
self.last_layer = self.deconv1
if self.out_distribution == True:
self.trunk_sigma = self.last_layer.log_sigma[:, :, :self.inpt.
shape[2], :]
self.trunc_output = self.tanh_out[:, :, :self.inpt.shape[2], :]
################################### FUNCTIONS ######################################################
self.get_latent_states = theano.function(
[self.inpt],
self.latent_out,
givens=[[self.dropout_symbolic, self.dropout_prob]])
#self.prior_debug = theano.function([self.inpt],[self.latent_out,self.latent_layer.mu_encoder,self.latent_layer.log_sigma_encoder,self.latent_layer.prior])
#self.get_prior = theano.function([self.inpt],self.latent_layer.prior)
#self.convolve1 = theano.function([self.inpt],self.layer1_out)
#self.convolve2 = theano.function([self.inpt],self.layer2_out)
self.output = theano.function(
[self.inpt],
self.trunc_output,
givens=[[self.dropout_symbolic, self.dropout_prob]])
self.get_flattened = theano.function(
[self.inpt],
self.flattened,
givens=[[self.dropout_symbolic, self.dropout_prob]])
#self.deconvolve1 = theano.function([self.inpt],self.deconv1.output)
#self.deconvolve2 = theano.function([self.inpt],self.deconv2.output)
#self.sig_out = theano.function([self.inpt],T.flatten(self.trunk_sigma,outdim=2))
self.output = theano.function(
[self.inpt],
self.trunc_output,
givens=[[self.dropout_symbolic, self.dropout_prob]])
#self.generate_from_z = theano.function([self.inpt],self.trunc_output,givens = [[self.latent_out,self.generative_z]])
self.generate_from_z = theano.function(
[self.inpt],
self.trunc_output,
givens=[[self.dropout_symbolic, self.dropout_prob],
[self.latent_out, self.generative_z]])
self.cost = self.MSE()
self.mse = self.MSE()
#self.likelihood = self.log_px_z()
#self.get_cost = theano.function([self.inpt],[self.cost,self.mse])
#self.get_likelihood = theano.function([self.layer1.inpt],[self.likelihood])
self.derivatives = T.grad(self.cost, self.params)
#self.get_gradients = theano.function([self.inpt],self.derivatives)
self.updates = adam(self.params, self.derivatives, self.learning_rate)
#self.updates =momentum_update(self.params,self.derivatives,self.learning_rate,self.momentum)
self.train_model = theano.function(
inputs=[self.inpt, self.df],
outputs=self.cost,
updates=self.updates,
givens=[[self.dropout_symbolic, self.dropout_prob]])
def get_parent_state(self, children_states, node_type, use_dropout: bool,
iteration_number) -> tuple:
w = self.__w_with_dropout if use_dropout else self.__w
return T.tanh(
T.dot(w[node_type], T.flatten(children_states)) +
self.__bias[node_type]), 0
def __init__(self, config, testMode):
self.config = config
batch_size = config['batch_size']
lib_conv = config['lib_conv']
useLayers = config['useLayers']
#imgWidth = config['imgWidth']
#imgHeight = config['imgHeight']
initWeights = config['initWeights'] #if we wish to initialize alexnet with some weights. #need to make changes in layers.py to accept initilizing weights
if initWeights:
weightsDir = config['weightsDir']
weightFileTag = config['weightFileTag']
prob_drop = config['prob_drop']
# ##################### BUILD NETWORK ##########################
x = T.ftensor4('x')
mean = T.ftensor4('mean')
#y = T.lvector('y')
print '... building the model'
self.layers = []
params = []
weight_types = []
if useLayers >= 1:
convpool_layer1 = ConvPoolLayer(input=x-mean,
image_shape=(3, None, None, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4, padsize=0, group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=True,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_0'+weightFileTag, 'b_0'+weightFileTag]
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
if useLayers >= 2:
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, None, None, batch_size), #change from 27 to appropriate value sbased on conv1's output
filter_shape=(96, 5, 5, 256),
convstride=1, padsize=2, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=True,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W0_1'+weightFileTag, 'W1_1'+weightFileTag, 'b0_1'+weightFileTag, 'b1_1'+weightFileTag]
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
if useLayers >= 3:
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, None, None, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_2'+weightFileTag, 'b_2'+weightFileTag]
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
if useLayers >= 4:
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, None, None, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1, padsize=1, group=2,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W0_3'+weightFileTag, 'W1_3'+weightFileTag, 'b0_3'+weightFileTag, 'b1_3'+weightFileTag]
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
if useLayers >= 5:
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, None, None, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1, padsize=1, group=2,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W0_4'+weightFileTag, 'W1_4'+weightFileTag, 'b0_4'+weightFileTag, 'b1_4'+weightFileTag]
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
if useLayers >= 6:
fc_layer6_input = T.flatten(convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096, initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_5'+weightFileTag, 'b_5'+weightFileTag])
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
if testMode:
dropout_layer6 = fc_layer6
else:
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096, prob_drop=prob_drop)
if useLayers >= 7:
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096, initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_6'+weightFileTag, 'b_6'+weightFileTag])
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
if testMode:
dropout_layer6 = fc_layer7
else:
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096, prob_drop=prob_drop)
if useLayers >= 8:
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output, n_in=4096, n_out=1000, initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_7'+weightFileTag, 'b_7'+weightFileTag])
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.output = self.layers[useLayers-1]
self.params = params
self.x = x
self.mean = mean
self.weight_types = weight_types
self.batch_size = batch_size
self.useLayers = useLayers
self.outLayer = self.layers[useLayers-1]
meanVal = np.load(config['mean_file'])
meanVal = meanVal[:, :, :, np.newaxis].astype('float32') #x is 4d, with 'batch' number of images. meanVal has only '1' in the 'batch' dimension. subtraction wont work.
meanVal = np.tile(meanVal,(1,1,1,batch_size))
self.meanVal = meanVal
#meanVal = np.zeros([3,imgHeight,imgWidth,2], dtype='float32')
if useLayers >= 8: #if last layer is softmax, then its output is y_pred
finalOut = self.outLayer.y_pred
else:
finalOut = self.outLayer.output
self.forwardFunction = theano.function([self.x, In(self.mean, value=meanVal)], [finalOut])
def flatten(x):
return T.flatten(x)
def convolve(kerns,
kshp,
nkern,
images,
imgshp,
step=(1, 1),
bias=None,
mode='valid',
flatten=True):
"""Convolution implementation by sparse matrix multiplication.
:note: For best speed, put the matrix which you expect to be
smaller as the 'kernel' argument
"images" is assumed to be a matrix of shape batch_size x img_size,
where the second dimension represents each image in raster order
If flatten is "False", the output feature map will have shape:
.. code-block:: python
batch_size x number of kernels x output_size
If flatten is "True", the output feature map will have shape:
.. code-block:: python
batch_size x number of kernels * output_size
.. note::
IMPORTANT: note that this means that each feature map (image
generate by each kernel) is contiguous in memory. The memory
layout will therefore be: [ <feature_map_0> <feature_map_1>
... <feature_map_n>], where <feature_map> represents a
"feature map" in raster order
kerns is a 2D tensor of shape nkern x N.prod(kshp)
:param kerns: 2D tensor containing kernels which are applied at every pixel
:param kshp: tuple containing actual dimensions of kernel (not symbolic)
:param nkern: number of kernels/filters to apply.
nkern=1 will apply one common filter to all input pixels
:param images: tensor containing images on which to apply convolution
:param imgshp: tuple containing image dimensions
:param step: determines number of pixels between adjacent receptive fields
(tuple containing dx,dy values)
:param mode: 'full', 'valid' see CSM.evaluate function for details
:param sumdims: dimensions over which to sum for the tensordot operation.
By default ((2,),(1,)) assumes kerns is a nkern x kernsize
matrix and images is a batchsize x imgsize matrix
containing flattened images in raster order
:param flatten: flatten the last 2 dimensions of the output. By default,
instead of generating a batchsize x outsize x nkern tensor,
will flatten to batchsize x outsize*nkern
:return: out1, symbolic result
:return: out2, logical shape of the output img (nkern,heigt,width)
:TODO: test for 1D and think of how to do n-d convolutions
"""
# start by computing output dimensions, size, etc
kern_size = np.int64(np.prod(kshp))
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if np.size(imgshp) == 2:
imgshp = (1, ) + imgshp
# construct indices and index pointers for sparse matrix, which,
# when multiplied with input images will generate a stack of image
# patches
indices, indptr, spmat_shape, sptype, outshp = \
convolution_indices.conv_eval(imgshp, kshp, step, mode)
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices, indptr,
spmat_shape)
patches = (sparse.structured_dot(csc, images.T)).T
# compute output of linear classifier
pshape = tensor.stack([images.shape[0] * tensor.as_tensor(np.prod(outshp)),\
tensor.as_tensor(imgshp[0] * kern_size)])
patch_stack = tensor.reshape(patches, pshape, ndim=2)
# kern is of shape: nkern x ksize*number_of_input_features
# output is thus of shape: bsize*outshp x nkern
output = tensor.dot(patch_stack, kerns.T)
# add bias across each feature map (more efficient to do it now)
if bias is not None:
output += bias
# now to have feature maps in raster order ...
# go from bsize*outshp x nkern to bsize x nkern*outshp
newshp = tensor.stack([images.shape[0],\
tensor.as_tensor(np.prod(outshp)),\
tensor.as_tensor(nkern)])
tensout = tensor.reshape(output, newshp, ndim=3)
output = tensor.DimShuffle((False, ) * tensout.ndim, (0, 2, 1))(tensout)
if flatten:
output = tensor.flatten(output, 2)
return output, np.hstack((nkern, outshp))
def build_objective(model, deterministic=False, epsilon=1.e-7):
predictions = T.flatten(
nn.layers.get_output(model.l_out, deterministic=deterministic))
targets = T.flatten(nn.layers.get_output(model.l_target))
preds = T.clip(predictions, epsilon, 1. - epsilon)
return T.mean(nn.objectives.binary_crossentropy(preds, targets))
def op(self, state):
X = self.l_in.op(state=state)
return T.flatten(X, outdim=self.axes)
def build_computation_graph(self):
###################### BUILD NETWORK ##########################
# whether or not to mirror the input images before feeding them into the network
if self.flag_datalayer:
layer_1_input = mirror_images(
input=self.x,
image_shape=(
self.batch_size,
3,
256,
256,
), # bc01 format
cropsize=227,
rand=self.rand,
flag_rand=self.rand_crop)
else:
layer_1_input = self.x # 4D tensor (going to be in c01b format)
# Start with 5 convolutional pooling layers
log.debug("convpool layer 1...")
convpool_layer1 = ConvPoolLayer(inputs_hook=((self.batch_size, 3, 227,
227), layer_1_input),
filter_shape=(96, 3, 11, 11),
convstride=4,
padsize=0,
group=1,
poolsize=3,
poolstride=2,
bias_init=0.0,
local_response_normalization=True)
# Add this layer's parameters!
self.params += convpool_layer1.get_params()
log.debug("convpool layer 2...")
convpool_layer2 = ConvPoolLayer(inputs_hook=((
self.batch_size,
96,
27,
27,
), convpool_layer1.get_outputs()),
filter_shape=(256, 96, 5, 5),
convstride=1,
padsize=2,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.1,
local_response_normalization=True)
# Add this layer's parameters!
self.params += convpool_layer2.get_params()
log.debug("convpool layer 3...")
convpool_layer3 = ConvPoolLayer(
inputs_hook=((self.batch_size, 256, 13, 13),
convpool_layer2.get_outputs()),
filter_shape=(384, 256, 3, 3),
convstride=1,
padsize=1,
group=1,
poolsize=1,
poolstride=0,
bias_init=0.0,
local_response_normalization=False)
# Add this layer's parameters!
self.params += convpool_layer3.get_params()
log.debug("convpool layer 4...")
convpool_layer4 = ConvPoolLayer(
inputs_hook=((self.batch_size, 384, 13, 13),
convpool_layer3.get_outputs()),
filter_shape=(384, 384, 3, 3),
convstride=1,
padsize=1,
group=2,
poolsize=1,
poolstride=0,
bias_init=0.1,
local_response_normalization=False)
# Add this layer's parameters!
self.params += convpool_layer4.get_params()
log.debug("convpool layer 5...")
convpool_layer5 = ConvPoolLayer(
inputs_hook=((self.batch_size, 384, 13, 13),
convpool_layer4.get_outputs()),
filter_shape=(256, 384, 3, 3),
convstride=1,
padsize=1,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.0,
local_response_normalization=False)
# Add this layer's parameters!
self.params += convpool_layer5.get_params()
# Now onto the fully-connected layers!
fc_config = {
'activation':
'rectifier', # type of activation function to use for output
'weights_init':
'gaussian', # either 'gaussian' or 'uniform' - how to initialize weights
'weights_mean': 0.0, # mean for gaussian weights init
'weights_std':
0.005, # standard deviation for gaussian weights init
'bias_init': 0.0 # how to initialize the bias parameter
}
log.debug("fully connected layer 1 (model layer 6)...")
# we want to have dropout applied to the training version, but not the test version.
fc_layer6_input = T.flatten(convpool_layer5.get_outputs(), 2)
fc_layer6 = BasicLayer(inputs_hook=(9216, fc_layer6_input),
output_size=4096,
config=fc_config)
# Add this layer's parameters!
self.params += fc_layer6.get_params()
# now apply dropout to the output for training
dropout_layer6 = dropout(fc_layer6.get_outputs(), corruption_level=0.5)
log.debug("fully connected layer 2 (model layer 7)...")
fc_layer7 = BasicLayer(inputs_hook=(4096, fc_layer6.get_outputs()),
output_size=4096,
config=fc_config)
fc_layer7_train = BasicLayer(inputs_hook=(4096, dropout_layer6),
output_size=4096,
params_hook=fc_layer7.get_params(),
config=fc_config)
# Add this layer's parameters!
self.params += fc_layer7_train.get_params()
# apply dropout again for training
dropout_layer7 = dropout(fc_layer7_train.get_outputs(),
corruption_level=0.5)
# last layer is a softmax prediction output layer
softmax_config = {
'weights_init': 'gaussian',
'weights_mean': 0.0,
'weights_std': 0.005,
'bias_init': 0.0
}
log.debug("softmax classification layer (model layer 8)...")
softmax_layer8 = SoftmaxLayer(inputs_hook=(4096,
fc_layer7.get_outputs()),
output_size=1000,
config=softmax_config)
softmax_layer8_train = SoftmaxLayer(
inputs_hook=(4096, dropout_layer7),
output_size=1000,
params_hook=softmax_layer8.get_params(),
config=softmax_config)
# Add this layer's parameters!
self.params += softmax_layer8.get_params()
# finally the softmax output from the whole thing!
self.output = softmax_layer8.get_outputs()
#####################
# Cost and monitors #
#####################
self.train_cost = softmax_layer8_train.negative_log_likelihood(self.y)
cost = softmax_layer8.negative_log_likelihood(self.y)
errors = softmax_layer8.errors(self.y)
train_errors = softmax_layer8_train.errors(self.y)
self.monitors = OrderedDict([('cost', cost), ('errors', errors),
('dropout_errors', train_errors)])
#########################
# Compile the functions #
#########################
log.debug("Compiling functions!")
t = time.time()
log.debug("f_predict...")
# use the actual argmax from the classification
self.f_predict = function(
inputs=[self.x], outputs=softmax_layer8.get_argmax_prediction())
log.debug("f_monitors")
self.f_monitors = function(inputs=[self.x, self.y],
outputs=self.monitors.values())
log.debug("compilation took %s" %
make_time_units_string(time.time() - t))
def gradient1(f, v):
"""flat gradient of f wrt v"""
return tt.flatten(tt.grad(f, v, disconnected_inputs="warn"))
def get_output_for(self, inputs, **kwargs):
"""
Have to re-write LSTMLayer's output construction because we need
cell_out, which is not stored in the original
"""
# Retrieve the layer input
input = inputs[0]
# Retrieve the mask when it is supplied
mask = None
hid_init = None
cell_init = None
if self.mask_incoming_index > 0:
mask = inputs[self.mask_incoming_index]
if self.hid_init_incoming_index > 0:
hid_init = inputs[self.hid_init_incoming_index]
if self.cell_init_incoming_index > 0:
cell_init = inputs[self.cell_init_incoming_index]
# Treat all dimensions after the second as flattened feature dimensions
if input.ndim > 3:
input = T.flatten(input, 3)
# Because scan iterates over the first dimension we dimshuffle to
# (n_time_steps, n_batch, n_features)
input = input.dimshuffle(1, 0, 2)
seq_len, num_batch, _ = input.shape
# Stack input weight matrices into a (num_inputs, 4*num_units)
# matrix, which speeds up computation
W_in_stacked = T.concatenate([
self.W_in_to_ingate, self.W_in_to_forgetgate, self.W_in_to_cell,
self.W_in_to_outgate
],
axis=1)
# Same for hidden weight matrices
W_hid_stacked = T.concatenate([
self.W_hid_to_ingate, self.W_hid_to_forgetgate, self.W_hid_to_cell,
self.W_hid_to_outgate
],
axis=1)
# Stack biases into a (4*num_units) vector
b_stacked = T.concatenate(
[self.b_ingate, self.b_forgetgate, self.b_cell, self.b_outgate],
axis=0)
if self.precompute_input:
# Because the input is given for all time steps, we can
# precompute_input the inputs dot weight matrices before scanning.
# W_in_stacked is (n_features, 4*num_units). input is then
# (n_time_steps, n_batch, 4*num_units).
input = T.dot(input, W_in_stacked) + b_stacked
# At each call to scan, input_n will be (n_time_steps, 4*num_units).
# We define a slicing function that extract the input to each LSTM gate
def slice_w(x, n):
return x[:, n * self.num_units:(n + 1) * self.num_units]
# Create single recurrent computation step function
# input_n is the n'th vector of the input
def step(input_n, cell_previous, hid_previous, *args):
if not self.precompute_input:
input_n = T.dot(input_n, W_in_stacked) + b_stacked
# Calculate gates pre-activations and slice
gates = input_n + T.dot(hid_previous, W_hid_stacked)
# Clip gradients
if self.grad_clipping:
gates = theano.gradient.grad_clip(gates, -self.grad_clipping,
self.grad_clipping)
# Extract the pre-activation gate values
ingate = slice_w(gates, 0)
forgetgate = slice_w(gates, 1)
cell_input = slice_w(gates, 2)
outgate = slice_w(gates, 3)
if self.peepholes:
# Compute peephole connections
ingate += cell_previous * self.W_cell_to_ingate
forgetgate += cell_previous * self.W_cell_to_forgetgate
# Apply nonlinearities
ingate = self.nonlinearity_ingate(ingate)
forgetgate = self.nonlinearity_forgetgate(forgetgate)
cell_input = self.nonlinearity_cell(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
if self.peepholes:
outgate += cell * self.W_cell_to_outgate
outgate = self.nonlinearity_outgate(outgate)
# Compute new hidden unit activation
hid = outgate * self.nonlinearity(cell)
return [cell, hid]
def step_masked(input_n, mask_n, cell_previous, hid_previous, *args):
cell, hid = step(input_n, cell_previous, hid_previous, *args)
# Skip over any input with mask 0 by copying the previous
# hidden state; proceed normally for any input with mask 1.
not_mask = 1 - mask_n
cell = cell * mask_n + cell_previous * not_mask
hid = hid * mask_n + hid_previous * not_mask
return [cell, hid]
if mask is not None:
# mask is given as (batch_size, seq_len). Because scan iterates
# over first dimension, we dimshuffle to (seq_len, batch_size) and
# add a broadcastable dimension
mask = mask.dimshuffle(1, 0, 'x')
sequences = [input, mask]
step_fun = step_masked
else:
sequences = input
step_fun = step
ones = T.ones((num_batch, 1))
if isinstance(self.cell_init, Layer):
pass
elif isinstance(self.cell_init, T.TensorVariable):
cell_init = self.cell_init
else:
# Dot against a 1s vector to repeat to shape (num_batch, num_units)
cell_init = T.dot(ones, self.cell_init)
if isinstance(self.hid_init, Layer):
pass
elif isinstance(self.hid_init, T.TensorVariable):
hid_init = self.hid_init
else:
# Dot against a 1s vector to repeat to shape (num_batch, num_units)
hid_init = T.dot(ones, self.hid_init)
# The hidden-to-hidden weight matrix is always used in step
non_seqs = [W_hid_stacked]
# The "peephole" weight matrices are only used when self.peepholes=True
if self.peepholes:
non_seqs += [
self.W_cell_to_ingate, self.W_cell_to_forgetgate,
self.W_cell_to_outgate
]
# When we aren't precomputing the input outside of scan, we need to
# provide the input weights and biases to the step function
if not self.precompute_input:
non_seqs += [W_in_stacked, b_stacked]
if self.unroll_scan:
# Retrieve the dimensionality of the incoming layer
input_shape = self.input_shapes[0]
# Explicitly unroll the recurrence instead of using scan
cell_out, hid_out = unroll_scan(fn=step_fun,
sequences=sequences,
outputs_info=[cell_init, hid_init],
go_backwards=self.backwards,
non_sequences=non_seqs,
n_steps=input_shape[1])
else:
# Scan op iterates over first dimension of input and repeatedly
# applies the step function
cell_out, hid_out = theano.scan(
fn=step_fun,
sequences=sequences,
outputs_info=[cell_init, hid_init],
go_backwards=self.backwards,
truncate_gradient=self.gradient_steps,
non_sequences=non_seqs,
strict=True)[0]
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if self.only_return_final:
hid_out = hid_out[-1]
cell_out = cell_out[-1]
else:
# dimshuffle back to (n_batch, n_time_steps, n_features))
hid_out = hid_out.dimshuffle(1, 0, 2)
cell_out = cell_out.dimshuffle(1, 0, 2)
# if scan is backward reverse the output
if self.backwards:
hid_out = hid_out[:, ::-1]
cell_out = cell_out[:, ::-1]
return T.concatenate([cell_out, hid_out], axis=2)
def get_output_for(self, inputs, **kwargs):
"""
Compute this layer's output function given a symbolic input variable
Parameters
----------
inputs : list of theano.TensorType
`inputs[0]` should always be the symbolic input variable. When
this layer has a mask input (i.e. was instantiated with
`mask_input != None`, indicating that the lengths of sequences in
each batch vary), `inputs` should have length 2, where `inputs[1]`
is the `mask`. The `mask` should be supplied as a Theano variable
denoting whether each time step in each sequence in the batch is
part of the sequence or not. `mask` should be a matrix of shape
``(n_batch, n_time_steps)`` where ``mask[i, j] = 1`` when ``j <=
(length of sequence i)`` and ``mask[i, j] = 0`` when ``j > (length
of sequence i)``. When the hidden state of this layer is to be
pre-filled (i.e. was set to a :class:`Layer` instance) `inputs`
should have length at least 2, and `inputs[-1]` is the hidden state
to prefill with. When the cell state of this layer is to be
pre-filled (i.e. was set to a :class:`Layer` instance) `inputs`
should have length at least 2, and `inputs[-1]` is the hidden state
to prefill with. When both the cell state and the hidden state are
being pre-filled `inputs[-2]` is the hidden state, while
`inputs[-1]` is the cell state.
Returns
-------
layer_output : theano.TensorType
Symbolic output variable.
"""
# Retrieve the layer input
input = inputs[0]
# Retrieve the mask when it is supplied
mask = None
hid_init = None
cell_init = None
if self.mask_incoming_index > 0:
mask = inputs[self.mask_incoming_index]
if self.hid_init_incoming_index > 0:
hid_init = inputs[self.hid_init_incoming_index]
if self.cell_init_incoming_index > 0:
cell_init = inputs[self.cell_init_incoming_index]
# Treat all dimensions after the second as flattened feature dimensions
if input.ndim > 3:
input = T.flatten(input, 3)
# Because scan iterates over the first dimension we dimshuffle to
# (n_time_steps, n_batch, n_features)
input = input.dimshuffle(1, 0, 2)
seq_len, num_batch, _ = input.shape
# Same for hidden weight matrices
W_hid_stacked = T.concatenate(
[self.W_hid_to_ingate, self.W_hid_to_forgetgate,
self.W_hid_to_cell, self.W_hid_to_outgate], axis=1)
if self.precompute_input:
# Because the input is given for all time steps, we can
# precompute_input the inputs dot weight matrices before scanning.
# W_in_stacked is (n_features, 4*num_units). input is then
# (n_time_steps, n_batch, 4*num_units).
# Stack input weight matrices into a (num_inputs, 4*num_units)
# matrix, which speeds up computation
W_in_stacked = T.concatenate(
[self.W_in_to_ingate, self.W_in_to_forgetgate,
self.W_in_to_cell, self.W_in_to_outgate], axis=1)
if not self.batch_norm:
# Stack biases into a (4*num_units) vector
b_stacked = T.concatenate(
[self.b_ingate, self.b_forgetgate,
self.b_cell, self.b_outgate], axis=0)
input = T.dot(input, W_in_stacked) + b_stacked
else:
input = self.bn.get_output_for(T.dot(input, W_in_stacked), mask, **kwargs)
else:
# Stack input weight matrices into a (num_inputs, 4*num_units)
# matrix, which speeds up computation
W_in_stacked = T.concatenate(
[self.W_in_to_ingate, self.W_in_to_forgetgate,
self.W_in_to_cell, self.W_in_to_outgate], axis=1)
# Stack biases into a (4*num_units) vector
b_stacked = T.concatenate(
[self.b_ingate, self.b_forgetgate,
self.b_cell, self.b_outgate], axis=0)
# At each call to scan, input_n will be (n_time_steps, 4*num_units).
# We define a slicing function that extract the input to each LSTM gate
def slice_w(x, n):
return x[:, n*self.num_units:(n+1)*self.num_units]
# Create single recurrent computation step function
# input_n is the n'th vector of the input
def step(input_n, cell_previous, hid_previous, *args):
if not self.precompute_input:
input_n = T.dot(input_n, W_in_stacked) + b_stacked
# Calculate gates pre-activations and slice
gates = input_n + T.dot(hid_previous, W_hid_stacked)
# Clip gradients
if self.grad_clipping:
gates = theano.gradient.grad_clip(
gates, -self.grad_clipping, self.grad_clipping)
# Extract the pre-activation gate values
ingate = slice_w(gates, 0)
forgetgate = slice_w(gates, 1)
cell_input = slice_w(gates, 2)
outgate = slice_w(gates, 3)
if self.peepholes:
# Compute peephole connections
ingate += cell_previous*self.W_cell_to_ingate
forgetgate += cell_previous*self.W_cell_to_forgetgate
# Apply nonlinearities
ingate = self.nonlinearity_ingate(ingate)
forgetgate = self.nonlinearity_forgetgate(forgetgate)
cell_input = self.nonlinearity_cell(cell_input)
# Compute new cell value
cell = forgetgate*cell_previous + ingate*cell_input
if self.peepholes:
outgate += cell*self.W_cell_to_outgate
outgate = self.nonlinearity_outgate(outgate)
# Compute new hidden unit activation
hid = outgate*self.nonlinearity(cell)
return [cell, hid]
def step_masked(input_n, mask_n, cell_previous, hid_previous, *args):
cell, hid = step(input_n, cell_previous, hid_previous, *args)
# Skip over any input with mask 0 by copying the previous
# hidden state; proceed normally for any input with mask 1.
cell = T.switch(mask_n, cell, cell_previous)
hid = T.switch(mask_n, hid, hid_previous)
return [cell, hid]
if mask is not None:
# mask is given as (batch_size, seq_len). Because scan iterates
# over first dimension, we dimshuffle to (seq_len, batch_size) and
# add a broadcastable dimension
mask = mask.dimshuffle(1, 0, 'x')
sequences = [input, mask]
step_fun = step_masked
else:
sequences = input
step_fun = step
ones = T.ones((num_batch, 1))
if not isinstance(self.cell_init, Layer):
# Dot against a 1s vector to repeat to shape (num_batch, num_units)
cell_init = T.dot(ones, self.cell_init)
if not isinstance(self.hid_init, Layer):
# Dot against a 1s vector to repeat to shape (num_batch, num_units)
hid_init = T.dot(ones, self.hid_init)
# The hidden-to-hidden weight matrix is always used in step
non_seqs = [W_hid_stacked]
# The "peephole" weight matrices are only used when self.peepholes=True
if self.peepholes:
non_seqs += [self.W_cell_to_ingate,
self.W_cell_to_forgetgate,
self.W_cell_to_outgate]
# When we aren't precomputing the input outside of scan, we need to
# provide the input weights and biases to the step function
if not self.precompute_input:
non_seqs += [W_in_stacked, b_stacked]
if self.unroll_scan:
# Retrieve the dimensionality of the incoming layer
input_shape = self.input_shapes[0]
# Explicitly unroll the recurrence instead of using scan
cell_out, hid_out = unroll_scan(
fn=step_fun,
sequences=sequences,
outputs_info=[cell_init, hid_init],
go_backwards=self.backwards,
non_sequences=non_seqs,
n_steps=input_shape[1])
else:
# Scan op iterates over first dimension of input and repeatedly
# applies the step function
cell_out, hid_out = theano.scan(
fn=step_fun,
sequences=sequences,
outputs_info=[cell_init, hid_init],
go_backwards=self.backwards,
truncate_gradient=self.gradient_steps,
non_sequences=non_seqs,
strict=True)[0]
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if self.only_return_final:
hid_out = hid_out[-1]
else:
# dimshuffle back to (n_batch, n_time_steps, n_features))
hid_out = hid_out.dimshuffle(1, 0, 2)
# if scan is backward reverse the output
if self.backwards:
hid_out = hid_out[:, ::-1]
return hid_out
def get_output_for(self, inputs, deterministic=False, **kwargs):
if not self.stochastic and not deterministic:
deterministic = True
print "deterministic mode: ", deterministic
def apply_regularization(weights, hid=False):
current_w0 = self.w0
if hid:
current_w0 = self.w0_hid
if self.mean_substraction_rounding:
return weights
elif self.mode == 'ternary':
return ternarize_weights(weights,
w0=current_w0,
deterministic=deterministic,
srng=self.srng)
elif self.mode == "binary":
return binarize_weights(weights,
1.,
self.srng,
deterministic=deterministic)
elif self.mode == "dual-copy":
return quantize_weights(weights,
srng=self.srng,
deterministic=deterministic)
else:
return weights
if self.round_input_weights:
self.Wb_in_to_hid = apply_regularization(self.W_in_to_hid)
if self.round_hid:
self.Wb_hid_to_hid = apply_regularization(self.W_hid_to_hid)
if self.round_bias:
self.bb = apply_regularization(self.b)
if self.round_input_weights:
Wr_in_to_hid = self.W_in_to_hid
if self.round_hid:
Wr_hid_to_hid = self.W_hid_to_hid
if self.round_bias:
br = self.b
if self.round_input_weights:
self.W_in_to_hid = self.Wb_in_to_hid
if self.round_hid:
self.W_hid_to_hid = self.Wb_hid_to_hid
if self.round_bias:
self.b = self.bb
input = inputs[0]
if self.batch_norm:
input = self.bn.get_output_for(input,
deterministic=deterministic,
**kwargs)
if len(inputs) > 1:
new_inputs = [input, inputs[1]]
else:
new_inputs = [input]
else:
new_inputs = inputs
inputs = new_inputs
input = inputs[0]
mask = None
hid_init = None
if self.mask_incoming_index > 0:
mask = inputs[self.mask_incoming_index]
if self.hid_init_incoming_index > 0:
hid_init = inputs[self.hid_init_incoming_index]
if input.ndim > 3:
input = T.flatten(input, 3)
input = input.dimshuffle(1, 0, 2)
seq_len, num_batch, _ = input.shape
W_in_stacked = T.concatenate([self.W_in_to_hid], axis=1)
W_hid_stacked = T.concatenate([self.W_hid_to_hid], axis=1)
b_stacked = T.concatenate([self.b], axis=0)
if self.precompute_input:
input = T.dot(input, W_in_stacked) + b_stacked
def step(input_n, hid_previous, *args):
hid_input = T.dot(hid_previous, W_hid_stacked)
if self.grad_clipping:
input_n = theano.gradient.grad_clip(input_n,
-self.grad_clipping,
self.grad_clipping)
hid_input = theano.gradient.grad_clip(hid_input,
-self.grad_clipping,
self.grad_clipping)
if not self.precompute_input:
input_n = T.dot(input_n, W_in_stacked) + b_stacked
hid = self.nonlinearity(hid_input + input_n)
return hid
def step_masked(input_n, mask_n, hid_previous, *args):
hid = step(input_n, hid_previous, *args)
hid = T.switch(mask_n, hid, hid_previous)
return hid
if mask is not None:
mask = mask.dimshuffle(1, 0, 'x')
sequences = [input, mask]
step_fun = step_masked
else:
sequences = [input]
step_fun = step
if not isinstance(self.hid_init, lasagne.layers.Layer):
hid_init = T.dot(T.ones((num_batch, 1)), self.hid_init)
non_seqs = [W_hid_stacked]
if not self.precompute_input:
non_seqs += [W_in_stacked, b_stacked]
if self.unroll_scan:
input_shape = self.input_shapes[0]
hid_out = lasagne.utils.unroll_scan(fn=step_fun,
sequences=sequences,
outputs_info=[hid_init],
go_backwards=self.backwards,
non_sequences=non_seqs,
n_steps=input_shape[1])[0]
else:
hid_out = theano.scan(fn=step_fun,
sequences=sequences,
go_backwards=self.backwards,
outputs_info=[hid_init],
non_sequences=non_seqs,
truncate_gradient=self.gradient_steps,
strict=True)[0]
if self.only_return_final:
hid_out = hid_out[-1]
else:
hid_out = hid_out.dimshuffle(1, 0, 2)
if self.backwards:
hid_out = hid_out[:, ::-1]
if self.round_input_weights:
self.W_in_to_hid = Wr_in_to_hid
if self.round_hid:
self.W_hid_to_hid = Wr_hid_to_hid
if self.round_bias:
self.b = br
return hid_out
def conv2d(
input,
filters,
image_shape=None,
filter_shape=None,
border_mode="valid",
subsample=(1, 1),
**kargs,
):
"""
signal.conv.conv2d performs a basic 2D convolution of the input with the
given filters. The input parameter can be a single 2D image or a 3D tensor,
containing a set of images. Similarly, filters can be a single 2D filter or
a 3D tensor, corresponding to a set of 2D filters.
Shape parameters are optional and will result in faster execution.
Parameters
----------
input : Symbolic theano tensor for images to be filtered.
Dimensions: ([num_images], image height, image width)
filters : Symbolic theano tensor for convolution filter(s).
Dimensions: ([num_filters], filter height, filter width)
border_mode: {'valid', 'full'}
See scipy.signal.convolve2d.
subsample
Factor by which to subsample output.
image_shape : tuple of length 2 or 3
([num_images,] image height, image width).
filter_shape : tuple of length 2 or 3
([num_filters,] filter height, filter width).
kwargs
See theano.tensor.nnet.conv.conv2d.
Returns
-------
symbolic 2D,3D or 4D tensor
Tensor of filtered images, with shape
([number images,] [number filters,] image height, image width).
"""
assert input.ndim in (2, 3)
assert filters.ndim in (2, 3)
# use shape information if it is given to us ###
if filter_shape and image_shape:
if input.ndim == 3:
bsize = image_shape[0]
else:
bsize = 1
imshp = (1, ) + tuple(image_shape[-2:])
if filters.ndim == 3:
nkern = filter_shape[0]
else:
nkern = 1
kshp = filter_shape[-2:]
else:
nkern, kshp = None, None
bsize, imshp = None, None
# reshape tensors to 4D, for compatibility with ConvOp ###
if input.ndim == 3:
sym_bsize = input.shape[0]
else:
sym_bsize = 1
if filters.ndim == 3:
sym_nkern = filters.shape[0]
else:
sym_nkern = 1
new_input_shape = tensor.join(0, tensor.stack([sym_bsize, 1]),
input.shape[-2:])
input4D = tensor.reshape(input, new_input_shape, ndim=4)
new_filter_shape = tensor.join(0, tensor.stack([sym_nkern, 1]),
filters.shape[-2:])
filters4D = tensor.reshape(filters, new_filter_shape, ndim=4)
# perform actual convolution ###
op = conv.ConvOp(
output_mode=border_mode,
dx=subsample[0],
dy=subsample[1],
imshp=imshp,
kshp=kshp,
nkern=nkern,
bsize=bsize,
**kargs,
)
output = op(input4D, filters4D)
# flatten to 3D tensor if convolving with single filter or single image
if input.ndim == 2 and filters.ndim == 2:
if theano.config.warn.signal_conv2d_interface:
warnings.warn(
"theano.tensor.signal.conv2d() now outputs a 2d tensor when both"
" inputs are 2d. To disable this warning, set the Theano flag"
" warn.signal_conv2d_interface to False",
stacklevel=3,
)
output = tensor.flatten(output.T, ndim=2).T
elif input.ndim == 2 or filters.ndim == 2:
output = tensor.flatten(output.T, ndim=3).T
return output
import numpy as np
import keras.backend as K
# A test script to validate causal dilated convolutions
dilation = 2
input = T.fvector()
filters = T.fvector(
) # (output channels, input channels, filter rows, filter columns).
input_reshaped = T.reshape(input, (1, -1, 1))
input_reshaped = K.temporal_pre_padding(input_reshaped, padding=dilation)
input_reshaped = T.reshape(input_reshaped, (1, 1, -1, 1))
filters_reshaped = T.reshape(filters, (1, 1, -1, 1))
out = T.nnet.conv2d(input_reshaped,
filters_reshaped,
border_mode='valid',
filter_dilation=(dilation, 1))
out = T.reshape(out, (1, -1, 1))
out = K.temporal_pre_padding(out, padding=dilation)
out = T.reshape(out, (1, 1, -1, 1))
out = T.nnet.conv2d(out,
filters_reshaped,
border_mode='valid',
filter_dilation=(dilation, 1))
out = T.flatten(out)
in_input = np.arange(8, dtype='float32')
in_filters = np.array([1, 1], dtype='float32')
f = theano.function([input, filters], out)
print "".join(["%3.0f" % i for i in in_input])
print "".join(["%3.0f" % i for i in f(in_input, in_filters)])
image_shape=x_shp,
filter_shape=w_fb.shape,
border_mode='valid')
s_P_sum = theano.shared(w_fb.sum(3).sum(2).sum(1))
Pmmm = p_mean * s_P_sum.dimshuffle(0, 'x', 'x')
s_PM = theano.shared((w_means * w_fb).sum(3).sum(2).sum(1))
z = p_scale * (Px - Pmmm) - s_PM.dimshuffle(0, 'x', 'x')
assert z.dtype == x.dtype, (z.dtype, x.dtype)
return z, (_shp[0], kN, _shp[2], _shp[3])
@pyll.scope.define
def slm_flatten((x, x_shp), ):
r = tensor.flatten(x, 2)
r_shp = x_shp[0], np.prod(x_shp[1:])
return r, r_shp
@pyll.scope.define_info(o_len=2)
def slm_lpool_smallgrid((x, x_shp), grid_res=2, order=1):
"""
Like lpool, but parametrized to produce a fixed size image as output.
The image is not rescaled, but rather single giant box filters are
defined for each output pixel, and stored in a matrix.
"""
assert x.dtype == 'float32'
order = float(order)
if hasattr(order, '__iter__'):
def get_output_for(self, inputs, **kwargs):
"""
Compute this layer's output function given a symbolic input variable
Parameters
----------
inputs : list of theano.TensorType
`inputs[0]` should always be the symbolic input variable. When
this layer has a mask input (i.e. was instantiated with
`mask_input != None`, indicating that the lengths of sequences in
each batch vary), `inputs` should have length 2, where `inputs[1]`
is the `mask`. The `mask` should be supplied as a Theano variable
denoting whether each time step in each sequence in the batch is
part of the sequence or not. `mask` should be a matrix of shape
``(n_batch, n_time_steps)`` where ``mask[i, j] = 1`` when ``j <=
(length of sequence i)`` and ``mask[i, j] = 0`` when ``j > (length
of sequence i)``. When the hidden state of this layer is to be
pre-filled (i.e. was set to a :class:`Layer` instance) `inputs`
should have length at least 2, and `inputs[-1]` is the hidden state
to prefill with. When the cell state of this layer is to be
pre-filled (i.e. was set to a :class:`Layer` instance) `inputs`
should have length at least 2, and `inputs[-1]` is the hidden state
to prefill with. When both the cell state and the hidden state are
being pre-filled `inputs[-2]` is the hidden state, while
`inputs[-1]` is the cell state.
Returns
-------
layer_output : theano.TensorType
Symbolic output variable.
"""
# Retrieve the layer input
input = inputs[0]
# Retrieve the mask when it is supplied
mask = None
hid_init = None
cell_init = None
encoder_hs = None
encoder_mask = None
if self.mask_incoming_index > 0:
mask = inputs[self.mask_incoming_index]
if self.hid_init_incoming_index > 0:
hid_init = inputs[self.hid_init_incoming_index]
if self.encoder_mask_incoming_index > 0:
# (n_batch, n_time_steps)
encoder_mask = inputs[self.encoder_mask_incoming_index]
encoder_mask = encoder_mask.astype('float32')
cell_init = inputs[self.cell_init_incoming_index]
if self.attention:
# (n_batch, n_time_steps, n_features)
encoder_hs = cell_init[0]
# encoder_mask is # (n_batch, n_time_steps, 1)
encoder_hs = encoder_hs * encoder_mask.dimshuffle(0, 1, 'x')
cell_init = cell_init[1]
# Treat all dimensions after the second as flattened feature dimensions
if input.ndim > 3:
input = T.flatten(input, 3)
# Because scan iterates over the first dimension we dimshuffle to
# (n_time_steps, n_batch, n_features)
input = input.dimshuffle(1, 0, 2)
seq_len, num_batch, _ = input.shape
# Stack input weight matrices into a (num_inputs, 4*num_units)
# matrix, which speeds up computation
W_in_stacked = T.concatenate([
self.W_in_to_ingate, self.W_in_to_forgetgate, self.W_in_to_cell,
self.W_in_to_outgate
],
axis=1)
# Same for hidden weight matrices
W_hid_stacked = T.concatenate([
self.W_hid_to_ingate, self.W_hid_to_forgetgate, self.W_hid_to_cell,
self.W_hid_to_outgate
],
axis=1)
# Stack biases into a (4*num_units) vector
b_stacked = T.concatenate(
[self.b_ingate, self.b_forgetgate, self.b_cell, self.b_outgate],
axis=0)
if self.precompute_input:
# Because the input is given for all time steps, we can
# precompute_input the inputs dot weight matrices before scanning.
# W_in_stacked is (n_features, 4*num_units). input is then
# (n_time_steps, n_batch, 4*num_units).
input = T.dot(input, W_in_stacked) + b_stacked
# At each call to scan, input_n will be (n_time_steps, 4*num_units).
# We define a slicing function that extract the input to each LSTM gate
def slice_w(x, n):
return x[:, n * self.num_units:(n + 1) * self.num_units]
# Create single recurrent computation step function
# input_n is the n'th vector of the input
def step(input_n, cell_previous, hid_previous, previous_r, *args):
if not self.precompute_input:
input_n = T.dot(input_n, W_in_stacked) + b_stacked
# Calculate gates pre-activations and slice
gates = input_n + T.dot(hid_previous, W_hid_stacked)
# Clip gradients
if self.grad_clipping:
gates = theano.gradient.grad_clip(gates, -self.grad_clipping,
self.grad_clipping)
# Extract the pre-activation gate values
ingate = slice_w(gates, 0)
forgetgate = slice_w(gates, 1)
cell_input = slice_w(gates, 2)
outgate = slice_w(gates, 3)
if self.peepholes:
# Compute peephole connections
ingate += cell_previous * self.W_cell_to_ingate
forgetgate += cell_previous * self.W_cell_to_forgetgate
# Apply nonlinearities
ingate = self.nonlinearity_ingate(ingate)
forgetgate = self.nonlinearity_forgetgate(forgetgate)
cell_input = self.nonlinearity_cell(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
if self.peepholes:
outgate += cell * self.W_cell_to_outgate
outgate = self.nonlinearity_outgate(outgate)
# Compute new hidden unit activation
hid = outgate * self.nonlinearity(cell)
r = previous_r
if self.attention and self.word_by_word:
mh = T.dot(hid, self.W_h_attend) + T.dot(
previous_r, self.W_r_attend)
# mh is (n_batch, 1, n_features)
mh = mh.dimshuffle(0, 'x', 1)
M = T.dot(encoder_hs, self.W_y_attend) + mh
# (n_batch, n_time_steps, n_features)
M = nonlinearities.tanh(M)
# alpha is (n_batch, n_time_steps, 1)
alpha = T.dot(M, self.w_attend)
# now is (n_batch, n_time_steps)
alpha = T.flatten(alpha, 2)
# 0 after softmax is not 0, f**k, my mistake.
# when i > encoder_seq_len, fill alpha_i to -np.inf
# alpha = T.switch(encoder_mask, alpha, -np.inf)
alpha = T.nnet.softmax(alpha)
# apply encoder_mask to alpha
# encoder_mask is (n_batch, n_time_steps)
# when i > encoder_seq_len, alpha_i should be 0.
# actually not need mask, but in case of error
# alpha = alpha * encoder_mask
alpha = alpha.dimshuffle(0, 1, 'x')
weighted_encoder = T.sum(encoder_hs * alpha, axis=1)
r = weighted_encoder + nonlinearities.tanh(
T.dot(previous_r, self.W_t_attend))
return [cell, hid, r]
def step_masked(input_n, mask_n, cell_previous, hid_previous,
previous_r, *args):
cell, hid, r = step(input_n, cell_previous, hid_previous,
previous_r, *args)
# Skip over any input with mask 0 by copying the previous
# hidden state; proceed normally for any input with mask 1.
cell = T.switch(mask_n, cell, cell_previous)
hid = T.switch(mask_n, hid, hid_previous)
r = T.switch(mask_n, r, previous_r)
return [cell, hid, r]
if mask is not None:
# mask is given as (batch_size, seq_len). Because scan iterates
# over first dimension, we dimshuffle to (seq_len, batch_size) and
# add a broadcastable dimension
mask = mask.dimshuffle(1, 0, 'x')
sequences = [input, mask]
step_fun = step_masked
else:
sequences = input
step_fun = step
ones = T.ones((num_batch, 1))
if not isinstance(self.hid_init, Layer):
# Dot against a 1s vector to repeat to shape (num_batch, num_units)
hid_init = T.dot(ones, self.hid_init)
# The hidden-to-hidden weight matrix is always used in step
non_seqs = [W_hid_stacked]
# The "peephole" weight matrices are only used when self.peepholes=True
if self.peepholes:
non_seqs += [
self.W_cell_to_ingate, self.W_cell_to_forgetgate,
self.W_cell_to_outgate
]
# When we aren't precomputing the input outside of scan, we need to
# provide the input weights and biases to the step function
if not self.precompute_input:
non_seqs += [W_in_stacked, b_stacked]
r_init = T.dot(ones, self.r_init)
if self.attention and self.word_by_word:
non_seqs += [
self.W_y_attend,
self.W_h_attend,
self.W_r_attend,
self.w_attend,
self.W_t_attend,
encoder_hs,
# encoder_mask
]
# Scan op iterates over first dimension of input and repeatedly
# applies the step function
cell_out, hid_out, r_out = theano.scan(
fn=step_fun,
sequences=sequences,
outputs_info=[cell_init, hid_init, r_init],
go_backwards=self.backwards,
truncate_gradient=self.gradient_steps,
non_sequences=non_seqs,
strict=True)[0]
# (n_batch, n_features)
hid_N = hid_out[-1]
out = hid_N
if self.attention:
if self.word_by_word:
r_N = r_out[-1]
else:
mh = T.dot(hid_N, self.W_h_attend)
mh = mh.dimshuffle(0, 'x', 1)
M = T.dot(encoder_hs, self.W_y_attend) + mh
# (n_batch, n_time_steps, n_features)
M = nonlinearities.tanh(M)
alpha = T.dot(M, self.w_attend)
# (n_batch, n_time_steps)
alpha = T.flatten(alpha, 2)
# when i > encoder_seq_len, fill alpha_i to -np.inf
# alpha = T.switch(encoder_mask, alpha, -np.inf)
alpha = T.nnet.softmax(alpha)
# apply encoder_mask to alpha
# encoder_mask is (n_batch, n_time_steps)
# when i > encoder_seq_len, alpha_i should be 0.
# actually not need mask, but in case of error
# alpha = alpha * encoder_mask
alpha = alpha.dimshuffle(0, 1, 'x')
# (n_batch, n_features)
r_N = T.sum(encoder_hs * alpha, axis=1)
out = nonlinearities.tanh(
T.dot(r_N, self.W_p_attend) + T.dot(hid_N, self.W_x_attend))
return out
all_params = nn.layers.get_all_params(l_out)
if config.one_hot:
all_params = all_params[1:]
all_layers = nn.layers.get_all_layers(l_out)
num_params = nn.layers.count_params(l_out)
print(' number of parameters: %d' % num_params)
print(' layer output shapes:')
print('#params:')
print('output shape:')
for layer in all_layers:
name = layer.__class__.__name__
num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
num_param = num_param.__str__()
print(' %s %s %s' % (name, num_param, layer.output_shape))
y = T.cast(T.flatten(x[:, 1:]), 'int32')
# training loss
p1 = T.reshape(T.log(predictions[T.arange(y.shape[0]), y]), mask.shape)
loss = -1. * T.mean(T.sum(mask * p1, axis=1), axis=0)
# validation loss (with disabled dropout)
p1_det = T.reshape(T.log(predictions_det[T.arange(y.shape[0]), y]), mask.shape)
loss_det = -1. * T.mean(T.sum(mask * p1_det, axis=1), axis=0)
learning_rate = theano.shared(np.float32(config.learning_rate))
grads = theano.grad(loss, all_params)
updates = nn.updates.rmsprop(grads, all_params, config.learning_rate)
train = theano.function([x, mask], loss, updates=updates)
validate = theano.function([x, mask], loss_det)
def main(data_sets, W_embed):
# Optimization learning rate
LEARNING_RATE = theano.shared(np.array(0.001, dtype=theano.config.floatX))
eta_decay = np.array(0.5, dtype=theano.config.floatX)
# Min/max sequence length
MAX_LENGTH = 300
X_raw_data, Y_raw_data = data_sets.get_data_from_type("train")
trainingAdmiSeqs, trainingMask, trainingLabels, trainingLengths, ltr = prepare_data(
X_raw_data, Y_raw_data, vocabsize=619, maxlen=MAX_LENGTH)
Num_Samples, MAX_LENGTH, N_VOCAB = trainingAdmiSeqs.shape
X_valid_data, Y_valid_data = data_sets.get_data_from_type("valid")
validAdmiSeqs, validMask, validLabels, validLengths, lval = prepare_data(
X_valid_data, Y_valid_data, vocabsize=619, maxlen=MAX_LENGTH)
X_test_data, Y_test_data = data_sets.get_data_from_type("test")
test_admiSeqs, test_mask, test_labels, testLengths, ltes = prepare_data(
X_test_data, Y_test_data, vocabsize=619, maxlen=MAX_LENGTH)
alllength = sum(trainingLengths) + sum(validLengths) + sum(testLengths)
print(alllength)
eventNum = sum(ltr) + sum(lval) + sum(ltes)
print(eventNum)
print("Building network ...")
N_BATCH = 1
# First, we build the network, starting with an input layer
# Recurrent layers expect input of shape
# (batch size, max sequence length, number of features)
l_in = lasagne.layers.InputLayer(shape=(N_BATCH, MAX_LENGTH, N_VOCAB))
#l_label = lasagne.layers.InputLayer(shape=(N_BATCH, MAX_LENGTH, 1))
# The network also needs a way to provide a mask for each sequence. We'll
# use a separate input layer for that. Since the mask only determines
# which indices are part of the sequence for each batch entry, they are
# supplied as matrices of dimensionality (N_BATCH, MAX_LENGTH)
l_mask = lasagne.layers.InputLayer(shape=(N_BATCH, MAX_LENGTH))
embedsize = 100
n_topics = 50
#l_embed = lasagne.layers.DenseLayer(l_in, num_units=embedsize, b=None, W = W_embed, num_leading_axes=2)
l_embed = lasagne.layers.DenseLayer(l_in,
num_units=embedsize,
b=None,
num_leading_axes=2)
#l_embed.params[l_embed.W].remove("trainable")
#l_drop = lasagne.layers.dropout(l_embed)
l_forward0 = lasagne.layers.GRULayer(l_embed,
N_HIDDEN,
mask_input=l_mask,
grad_clipping=GRAD_CLIP,
only_return_final=False)
l_forward = MaskingLayer([l_forward0, l_mask])
l_1 = lasagne.layers.DenseLayer(
l_in,
num_units=N_HIDDEN,
nonlinearity=lasagne.nonlinearities.rectify,
num_leading_axes=2)
l_2 = lasagne.layers.DenseLayer(
l_1,
num_units=N_HIDDEN,
nonlinearity=lasagne.nonlinearities.rectify,
num_leading_axes=2)
mu = lasagne.layers.DenseLayer(l_2,
num_units=n_topics,
nonlinearity=None,
num_leading_axes=1) # batchsize * n_topic
log_sigma = lasagne.layers.DenseLayer(
l_2, num_units=n_topics, nonlinearity=None,
num_leading_axes=1) # batchsize * n_topic
l_theta = ThetaLayer([mu, log_sigma],
maxlen=MAX_LENGTH) #batchsize * maxlen * n_topic
l_B = lasagne.layers.DenseLayer(l_in,
b=None,
num_units=n_topics,
nonlinearity=None,
num_leading_axes=2)
l_context = lasagne.layers.ElemwiseMergeLayer([l_B, l_theta], T.mul)
l_context = lasagne.layers.ExpressionLayer(l_context,
lambda X: X.mean(-1),
output_shape="auto")
l_dense0 = lasagne.layers.DenseLayer(l_forward,
num_units=1,
nonlinearity=None,
num_leading_axes=2)
l_dense1 = lasagne.layers.reshape(l_dense0,
([0], [1])) #batchsize * maxlen
l_dense = lasagne.layers.ElemwiseMergeLayer([l_dense1, l_context], T.add)
l_out0 = lasagne.layers.NonlinearityLayer(
l_dense, nonlinearity=lasagne.nonlinearities.sigmoid)
l_out = lasagne.layers.ExpressionLayer(
lasagne.layers.ElemwiseMergeLayer([l_out0, l_mask], T.mul),
lambda X: X + 0.000001)
target_values = T.matrix('target_output')
target_values_flat = T.flatten(target_values)
# lasagne.layers.get_output produces a variable for the output of the net
network_output = lasagne.layers.get_output(l_out)
# The network output will have shape (n_batch, maxlen); let's flatten to get a
# 1-dimensional vector of predicted values
predicted_values = network_output.flatten()
# Our cost will be mean-squared error
cost = lasagne.objectives.binary_crossentropy(predicted_values,
target_values_flat)
kl_term = l_theta.klterm
cost = cost.sum() + kl_term
test_output = lasagne.layers.get_output(l_out, deterministic=True)
#cost = T.mean((predicted_values - target_values)**2)
# Retrieve all parameters from the network
all_params = lasagne.layers.get_all_params(l_out)
# Compute SGD updates for training
print("Computing updates ...")
updates = lasagne.updates.adam(cost, all_params, LEARNING_RATE)
# Theano functions for training and computing cost
print("Compiling functions ...")
train = theano.function([l_in.input_var, target_values, l_mask.input_var],
cost,
updates=updates)
compute_cost = theano.function(
[l_in.input_var, target_values, l_mask.input_var], cost)
prd = theano.function([l_in.input_var, l_mask.input_var], test_output)
#rnn_out = T.concatenate(l_theta.theta, lasagne.layers.get_output(l_forward0)[:,-1,:].reshape((N_BATCH, N_HIDDEN)),axis=1)
output_theta = theano.function([l_in.input_var, l_mask.input_var], [
l_theta.theta,
lasagne.layers.get_output(l_forward0)[:, -1, :].reshape(
(N_BATCH, N_HIDDEN))
],
on_unused_input='ignore')
print("Training ...")
try:
for epoch in range(num_epochs):
train_err = 0
train_batches = 0
start_time = time.time()
thetas_train = []
for batch in iterate_minibatches_listinputs(
[trainingAdmiSeqs, trainingLabels, trainingMask],
N_BATCH,
shuffle=True):
inputs = batch
train_err += train(inputs[0], inputs[1], inputs[2])
train_batches += 1
theta_train, rnnvec_train = output_theta(inputs[0], inputs[2])
rnnout_train = np.concatenate([theta_train, rnnvec_train],
axis=1)
thetas_train.append(rnnout_train.flatten())
if (train_batches + 1) % 1000 == 0:
print(train_batches)
np.save("theta_with_rnnvec/thetas_train" + str(epoch),
thetas_train)
# # And a full pass over the validation data:
# val_err = 0
# val_acc = 0
# val_batches = 0
# new_validlabels = []
# pred_validlabels = []
# for batch in iterate_minibatches_listinputs([validAdmiSeqs, validLabels, validMask, validLengths], 1, shuffle=False):
# inputs = batch
# err = compute_cost(inputs[0], inputs[1], inputs[2])
# val_err += err
# leng = inputs[3][0]
# new_validlabels.extend(inputs[1].flatten()[:leng])
# pred_validlabels.extend(prd(inputs[0], inputs[2]).flatten()[:leng])
# val_batches += 1
# val_auc = roc_auc_score(new_validlabels, pred_validlabels)
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs,
time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err /
train_batches))
# print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
# print(" validation auc:\t\t{:.6f}".format(val_auc))
# print(" validation accuracy:\t\t{:.2f} %".format(
# val_acc / val_batches * 100))
# After training, we compute and print the test error:
test_err = 0
test_batches = 0
new_testlabels = []
pred_testlabels = []
thetas = []
for batch in iterate_minibatches_listinputs(
[test_admiSeqs, test_labels, test_mask, testLengths],
1,
shuffle=False):
inputs = batch
err = compute_cost(inputs[0], inputs[1], inputs[2])
test_err += err
leng = inputs[3][0]
new_testlabels.extend(inputs[1].flatten()[:leng])
pred_testlabels.extend(
prd(inputs[0], inputs[2]).flatten()[:leng])
theta, rnnvec = output_theta(inputs[0], inputs[2])
rnnout = np.concatenate([theta, rnnvec], axis=1)
thetas.append(rnnout.flatten())
test_batches += 1
test_auc = roc_auc_score(new_testlabels, pred_testlabels)
test_pr_auc = pr_auc(new_testlabels, pred_testlabels)
# np.save("CONTENT_results/testlabels_"+str(epoch),new_testlabels)
# np.save("CONTENT_results/predlabels_"+str(epoch),pred_testlabels)
# np.save("CONTENT_results/thetas"+str(epoch),thetas)
# np.save("theta_with_rnnvec/testlabels_"+str(epoch),new_testlabels)
# np.save("theta_with_rnnvec/predlabels_"+str(epoch),pred_testlabels)
# np.save("theta_with_rnnvec/thetas"+str(epoch),thetas)
test_pre_rec_f1 = precision_recall_fscore_support(
np.array(new_testlabels),
np.array(pred_testlabels) > 0.5,
average='binary')
test_acc = accuracy_score(np.array(new_testlabels),
np.array(pred_testlabels) > 0.5)
print("Final results:")
print(" test loss:\t\t{:.6f}".format(test_err / test_batches))
print(" test auc:\t\t{:.6f}".format(test_auc))
print(" test pr_auc:\t\t{:.6f}".format(test_pr_auc))
print(" test accuracy:\t\t{:.2f} %".format(test_acc * 100))
print(
" test Precision, Recall and F1:\t\t{:.4f} %\t\t{:.4f}\t\t{:.4f}"
.format(test_pre_rec_f1[0], test_pre_rec_f1[1],
test_pre_rec_f1[2]))
except KeyboardInterrupt:
pass
def sparse_categorical_crossentropy(output, target, from_logits=False):
target = T.cast(T.flatten(target), 'int32')
target = T.extra_ops.to_one_hot(target, nb_class=output.shape[-1])
target = reshape(target, shape(output))
return categorical_crossentropy(output, target, from_logits)
def build_objective(model, deterministic=False, epsilon=1e-12):
predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
targets = T.cast(T.flatten(nn.layers.get_output(model.l_target)), 'int32')
cc = nn.objectives.categorical_crossentropy(predictions,targets)
return T.mean(cc)
def build_objective(model, deterministic=False, epsilon=1e-12):
p = nn.layers.get_output(model.l_out, deterministic=deterministic)
targets = T.flatten(nn.layers.get_output(model.l_target))
p = T.clip(p, epsilon, 1. - epsilon)
bce = T.nnet.binary_crossentropy(p, targets)
return T.mean(bce)
def max_pool(images, imgshp, maxpoolshp):
"""
Implements a max pooling layer
Takes as input a 2D tensor of shape batch_size x img_size and performs max pooling.
Max pooling downsamples by taking the max value in a given area, here defined by
maxpoolshp. Outputs a 2D tensor of shape batch_size x output_size.
Parameters
----------
images : 2D tensor
Tensorcontaining images on which to apply convolution. Assumed to be \
of shape `batch_size x img_size`
imgshp : tuple
Tuple containing image dimensions
maxpoolshp : tuple
Tuple containing shape of area to max pool over
Returns
-------
out1 : WRITEME
Symbolic result (2D tensor)
out2 : WRITEME
Logical shape of the output
"""
N = numpy
poolsize = N.int64(N.prod(maxpoolshp))
# imgshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if N.size(imgshp) == 2:
imgshp = (1, ) + imgshp
# construct indices and index pointers for sparse matrix, which, when multiplied
# with input images will generate a stack of image patches
indices, indptr, spmat_shape, sptype, outshp = \
convolution_indices.conv_eval(imgshp, maxpoolshp, maxpoolshp, mode='valid')
print 'XXXXXXXXXXXXXXXX MAX POOLING LAYER XXXXXXXXXXXXXXXXXXXX'
print 'imgshp = ', imgshp
print 'maxpoolshp = ', maxpoolshp
print 'outshp = ', outshp
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices, indptr,
spmat_shape)
patches = sparse.structured_dot(csc, images.T).T
pshape = tensor.stack(images.shape[0]*\
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(imgshp[0]),
tensor.as_tensor(poolsize))
patch_stack = tensor.reshape(patches, pshape, ndim=3)
out1 = tensor.max(patch_stack, axis=2)
pshape = tensor.stack(images.shape[0], tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(imgshp[0]))
out2 = tensor.reshape(out1, pshape, ndim=3)
out3 = tensor.DimShuffle((False, ) * 3, (0, 2, 1))(out2)
return tensor.flatten(out3, 2), outshp
