def build(self, input_shape=(28, 28, 1), classes=10):
inputs = keras.Input(shape=input_shape)
outputs = conv2d(filters=6, kernel_size=(6, 6))(inputs)
outputs = max_pooling2d(pool_size=(2, 2), strides=(2, 2))(outputs)
outputs = sigmoid()(outputs)
outputs = conv2d(filters=16, kernel_size=(6, 6))(inputs)
outputs = max_pooling2d(pool_size=(2, 2), strides=(2, 2))(outputs)
outputs = sigmoid()(outputs)
outputs = flatten()(outputs)
outputs = dense(120)(outputs)
outputs = sigmoid()(outputs)
outputs = dense(64)(outputs)
outputs = sigmoid()(outputs)
outputs = dense(classes)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model
def encoder(self, x, training=True, reuse=None, name=None):
# [None, 28, 28, 1] --> [None, 14, 14, 64]
h = conv2d(x, 64, kernel_size=4, strides=2, activation=tf.nn.leaky_relu, reuse=reuse, name='e_conv_1')
# [None, 14, 14, 64] --> [None, 7, 7, 128]
h = conv2d(h, 128, kernel_size=4, strides=2, reuse=reuse, name='e_conv_2')
h = batch_norm(h, training=training, reuse=reuse, name='e_bn_1')
h = tf.nn.leaky_relu(h)
# [None, 7, 7, 128] --> [None, 7*7*128]
h = tf.reshape(h, [-1, 7*7*128])
# [None, 7*7*128] --> [None, 1024]
h = dense(h, 1024, reuse=reuse, name='e_dense_1')
h = batch_norm(h, training=training, reuse=reuse, name='e_bn_2')
h = tf.nn.leaky_relu(h)
# [None, 1024] --> [None, 2*self.z_dim]
h = dense(h, 2*self.z_dim, reuse=reuse, name='e_dense_2')
# Assign names to final outputs
mean = tf.identity(h[:,:self.z_dim], name=name+"_mean")
log_sigma = tf.identity(h[:,self.z_dim:], name=name+"_log_sigma")
return mean, log_sigma
def decoder(state, training=True):
x = tf.reshape(state, [batch_size, -1])
x = lyr.dense('decoder.dense1.matrix', 'decoder.dense1.bias',
'decoder', latent_dim, 512, x)
x = tf.nn.leaky_relu(x)
x = lyr.batchnorm(x, 'decoder.batchnorm1.offset',
'decoder.batchnorm1.scale', 'decoder')
x = lyr.dense('decoder.dense2.matrix', 'decoder.dense2.bias',
'decoder', 512, 512, x)
x = tf.nn.leaky_relu(x)
x = lyr.batchnorm(x, 'decoder.batchnorm2.offset',
'decoder.batchnorm2.scale', 'decoder')
x = lyr.dense('decoder.dense3.matrix', 'decoder.dense3.bias',
'decoder', 512, 256, x)
x = tf.nn.leaky_relu(x)
x = lyr.batchnorm(x, 'decoder.batchnorm3.offset',
'decoder.batchnorm3.scale', 'decoder')
x = lyr.dense('decoder.dense4.matrix', 'decoder.dense4.bias',
'decoder', 256, max_size * encode_length, x)
x = tf.reshape(x, [batch_size, max_size, encode_length])
x = lyr.batchnorm(x, 'decoder.batchnorm4.offset',
'decoder.batchnorm4.scale', 'decoder')
return x
def _stochastic(self, x, dim, scope, ema):
b_init_var = tf.constant_initializer(0. if self.is_log_var else 1.)
x = self.activation(x)
if isinstance(dim, int):
flatten = tf.contrib.layers.flatten(x)
mean = dense(flatten,
dim,
scope=scope + "_mean",
training=self.ph_is_training,
ema=ema,
init=self.init)
var = dense(flatten,
dim,
scope=scope + "_var",
bias_initializer=b_init_var,
training=self.ph_is_training,
ema=ema,
init=self.init)
else:
mean = conv2d(x,
dim,
scope=scope + "_mean",
training=self.ph_is_training,
ema=ema,
init=self.init)
var = conv2d(x,
dim,
scope=scope + "_var",
bias_initializer=b_init_var,
training=self.ph_is_training,
ema=ema,
init=self.init)
var = tf.nn.softplus(var) + self.eps
z = stochastic_gaussian(mean, var, is_log_var=self.is_log_var)
return z, mean, var
def network(x, weights, biases):
x = tf.reshape(x, shape=[-1, input_size_h, input_size_w, num_channels])
x = tf.subtract(x, mean)
x = tf.divide(x, std)
x = tf.expand_dims(x, axis=1)
x = tf.transpose(x, perm=[0, 4, 2, 3, 1])
conv0 = tf.nn.conv3d(x, weights["wc0"], strides=[1, 1, 1, 1, 1], padding="SAME")
conv0 = tf.nn.bias_add(conv0, biases["bc0"])
conv0 = tf.nn.relu(conv0)
conv0 = tf.transpose(conv0, perm=[0, 1, 4, 2, 3])
conv0 = tf.reshape(conv0, shape=[-1, 12, 192, 256])
conv0 = tf.transpose(conv0, perm=[0, 2, 3, 1])
conv1 = conv2d(conv0, weights["wc1"], biases["bc1"], strides=2)
conv2 = conv2d(conv1, weights["wc2"], biases["bc2"], strides=2)
conv3 = conv2d(conv2, weights["wc3"], biases["bc3"], strides=2)
conv4 = conv2d(conv3, weights["wc4"], biases["bc4"], strides=2)
conv5 = conv2d(conv4, weights["wc5"], biases["bc5"], strides=2)
conv6 = conv2d(conv5, weights["wc6"], biases["bc6"], strides=2)
fc1 = flatten(conv6)
fc1 = dense(fc1, weights["wd1"], biases["bd1"])
fc2 = dense(fc1, weights["wd2"], biases["bd2"])
fc3 = dense(fc2, weights["wd3"], biases["bd3"])
out = tf.add(tf.matmul(fc3, weights["out"]), biases["bias_out"])
return out
def __init__(self, sess, input_shape, num_actions, reuse=False, is_training=True, name='train'):
super().__init__(sess, reuse)
self.initial_state = []
with tf.name_scope(name + "policy_input"):
self.X_input = tf.placeholder(tf.uint8, input_shape)
with tf.variable_scope("policy", reuse=reuse):
conv1 = conv2d('conv1', tf.cast(self.X_input, tf.float32) / 255., num_filters=32, kernel_size=(8, 8),
padding='VALID', stride=(4, 4),
initializer=orthogonal_initializer(np.sqrt(2)), activation=tf.nn.relu,
is_training=is_training)
conv2 = conv2d('conv2', conv1, num_filters=64, kernel_size=(4, 4), padding='VALID', stride=(2, 2),
initializer=orthogonal_initializer(np.sqrt(2)), activation=tf.nn.relu,
is_training=is_training)
conv3 = conv2d('conv3', conv2, num_filters=64, kernel_size=(3, 3), padding='VALID', stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)), activation=tf.nn.relu,
is_training=is_training)
conv3_flattened = flatten(conv3)
fc4 = dense('fc4', conv3_flattened, output_dim=512, initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu, is_training=is_training)
self.policy_logits = dense('policy_logits', fc4, output_dim=num_actions,
initializer=orthogonal_initializer(np.sqrt(1.0)), is_training=is_training)
self.value_function = dense('value_function', fc4, output_dim=1,
initializer=orthogonal_initializer(np.sqrt(1.0)), is_training=is_training)
with tf.name_scope('value'):
self.value_s = self.value_function[:, 0]
with tf.name_scope('action'):
self.action_s = noise_and_argmax(self.policy_logits)
def decoder(self, z, training=True, reuse=None, name=None):
# [None, z_dim] --> [None, 1024]
h = dense(z, 1024, reuse=reuse, name='d_dense_1')
h = batch_norm(h, training=training, reuse=reuse, name='d_bn_1')
h = tf.nn.relu(h)
# [None, 1024] --> [None, 7*7*128]
h = dense(h, self.min_res*self.min_res*self.min_chans, reuse=reuse, name='d_dense_2')
h = batch_norm(h, training=training, reuse=reuse, name='d_bn_2')
h = tf.nn.relu(h)
# [None, 7*7*128] --> [None, 7, 7, 128]
h = tf.reshape(h, [-1, self.min_res, self.min_res, self.min_chans])
# [None, 7, 7, 128] --> [None, 14, 14, 64]
h = conv2d_transpose(h, 64, kernel_size=4, strides=2, reuse=reuse, name='d_tconv_1')
h = batch_norm(h, training=training, reuse=reuse, name='d_bn_3')
h = tf.nn.relu(h)
# [None, 14, 14, 64] --> [None, 28, 28, 1]
h = conv2d_transpose(h, 1, kernel_size=4, strides=2, activation=tf.nn.sigmoid, reuse=reuse, name='d_tconv_2')
# Assign name to final output
return tf.identity(h, name=name)
def build_model(t_params, n_dim_img, n_dim_txt, n_dim_enc, n_dim_dec, n_dim_vocab, optimizer):
'''
Build the whole model for training
'''
x = tensor.tensor3('x', config.floatX)
mask_x = tensor.matrix('mask_x', 'int8')
# Encoder(s) and initialization of hidden layer
enc = gru(mask_x, dropout(x), t_params, n_dim_img, n_dim_enc, 'enc')[-1]
init_h = tensor.tanh(dense(enc, t_params, n_dim_enc, n_dim_dec, 'init_h'))
y = tensor.matrix('y', 'int32')
mask_y = tensor.matrix('mask_y', 'int8')
n_steps, n_samples = y.shape
# Word embedding
emb = embedding(y, t_params, n_dim_vocab, n_dim_txt, 'emb').reshape((n_steps, n_samples, n_dim_txt))[: -1]
emb = tensor.concatenate([tensor.zeros((1, n_samples, n_dim_txt), config.floatX), emb])
# Decoder(s)
dec = gru(mask_y, emb, t_params, n_dim_txt, n_dim_dec, 'dec', init_h=init_h)
# Full-connected layer
fc = dense(dropout(dec), t_params, n_dim_dec, n_dim_vocab, 'fc')
# Classifier
prob = tensor.nnet.softmax(fc.reshape((n_steps * n_samples, n_dim_vocab)))
# Cost function
cost = prob[tensor.arange(n_steps * n_samples), y.flatten()].reshape((n_steps, n_samples))
cost = ((-tensor.log(cost + 1e-6) * mask_y).sum(0) / mask_y.astype(config.floatX).sum(0)).mean()
grads = tensor.grad(cost, list(t_params.values()))
f_cost, f_update = optimizer(tensor.scalar('lr'), t_params, grads, [x, mask_x, y, mask_y], cost)
return f_cost, f_update
def __call__(self, x, reuse=True):
with tf.variable_scope(self.name) as vs:
if reuse:
vs.reuse_variables()
_x = dense(x, 500, activation_='lrelu')
_x = dense(_x, 500, activation_='lrelu')
_x = dense(_x, 1, activation_=None)
return _x
def __call__(self, x, reuse=False):
with tf.variable_scope(self.name) as vs:
if reuse:
vs.reuse_variables()
with tf.variable_scope('Encoder'):
_x = conv_block(x,
filters=16,
sampling='same',
**self.conv_block_params)
_x = conv_block(_x,
filters=16,
sampling='down',
**self.conv_block_params)
_x = conv_block(_x,
filters=32,
sampling='same',
**self.conv_block_params)
_x = conv_block(_x,
filters=32,
sampling='down',
**self.conv_block_params)
current_shape = _x.get_shape().as_list()[1:]
_x = flatten(_x)
_x = dense(_x, 512, activation_='lrelu')
encoded = dense(_x, self.latent_dim)
with tf.variable_scope('Decoder'):
_x = dense(encoded, 512, activation_='lrelu')
_x = dense(_x,
current_shape[0] * current_shape[1] *
current_shape[2],
activation_='lrelu')
_x = reshape(_x, current_shape)
_x = conv_block(_x,
filters=32,
sampling=self.upsampling,
**self.conv_block_params)
_x = conv_block(_x,
filters=16,
sampling='same',
**self.conv_block_params)
_x = conv_block(_x,
filters=16,
sampling=self.upsampling,
**self.conv_block_params)
_x = conv_block(_x,
filters=self.channel,
sampling='same',
**self.last_conv_block_params)
return encoded, _x
def add_prediction_op(self):
"""Applies a GRU RNN over the input data, then an affine layer projection. Steps to complete
in this function:
- Roll over inputs_placeholder with GRUCell, producing a Tensor of shape [batch_s, max_timestep,
hidden_size].
- Apply a W * f + b transformation over the data, where f is each hidden layer feature. This
should produce a Tensor of shape [batch_s, max_timesteps, num_classes]. Set this result to
"logits".
Remember:
* Use the xavier initialization for matrices (W, but not b).
* W should be shape [hidden_size, num_classes].
"""
# Non-recurrent hidden layers
inputs = self.inputs_placeholder
for i in range(self.config.num_hidden_layers):
with tf.variable_scope('hidden%d' % (i + 1)) as vs:
inputs = layers.dense(inputs=inputs,
output_size=self.config.hidden_size,
activation=tf.nn.relu)
# Construct forward and backward cells of bidirectional RNN
fwdcell = layers.FactorizedLSTMCell(
self.config.hidden_size,
num_proj=self.config.svd_rank,
activation=self.config.activation_func,
)
bckcell = layers.FactorizedLSTMCell(
self.config.hidden_size,
num_proj=self.config.svd_rank,
activation=self.config.activation_func,
)
# TODO: look into non-zero initial hidden states?
rnn_outputs, rnn_last_states = tf.nn.bidirectional_dynamic_rnn(
fwdcell,
bckcell,
inputs=inputs,
dtype=tf.float32,
sequence_length=self.seq_lens_placeholder)
# Reuse projection matrices
with tf.variable_scope('final'):
with tf.variable_scope('fw'):
fw_logits = layers.dense(
inputs=rnn_outputs[0],
output_size=self.config.num_classes,
bias=True,
)
with tf.variable_scope('bw'):
bw_logits = layers.dense(
inputs=rnn_outputs[1],
output_size=self.config.num_classes,
bias=False,
)
self.logits = fw_logits + bw_logits
def __call__(self, x):
x = tf.cast(x, dtype=tf.float32)
self.conv1 = ll.conv2dx(x, self.model_weights[0], 1) # 1st Layer
self.conv1 = ll.conv2dx(self.conv1, self.model_weights[1], 1)
self.conv1 = ll.conv2dx(self.conv1, self.model_weights[2], 1)
self.pool1 = ll.maxpool(self.conv1, 2, 2)
self.conv2 = ll.conv2dx(self.pool1, self.model_weights[3],
1) # 2nd Layer
self.conv2 = ll.conv2dx(self.conv2, self.model_weights[4], 1)
self.conv2 = ll.conv2dx(self.conv2, self.model_weights[5], 1)
self.pool2 = ll.maxpool(self.conv2, 2, 2)
self.conv3 = ll.conv2dx(self.pool2, self.model_weights[6],
1) # 3rd Layer
self.conv3 = ll.conv2dx(self.conv3, self.model_weights[7], 1)
self.conv3 = ll.conv2dx(self.conv3, self.model_weights[8], 1)
self.pool3 = ll.maxpool(self.conv3, 2, 2)
self.conv4 = ll.conv2dx(self.pool3, self.model_weights[9],
1) # 4th Layer
self.conv4 = ll.conv2dx(self.conv4, self.model_weights[10], 1)
self.conv4 = ll.conv2dx(self.conv4, self.model_weights[11], 1)
self.pool4 = ll.maxpool(self.conv4, 2, 2)
self.conv5 = ll.conv2dx(self.pool4, self.model_weights[12],
1) # 5th Layer
self.conv5 = ll.conv2dx(self.conv5, self.model_weights[13], 1)
self.conv5 = ll.conv2dx(self.conv5, self.model_weights[14], 1)
self.pool5 = ll.maxpool(self.conv5, 2, 2)
self.conv6 = ll.conv2dx(self.pool5, self.model_weights[15],
1) # 6th Layer
self.conv6 = ll.conv2dx(self.conv6, self.model_weights[16], 1)
self.conv6 = ll.conv2dx(self.conv6, self.model_weights[17], 1)
self.pool6 = ll.maxpool(self.conv6, 2, 2)
self.flatten_layer = tf.reshape(self.pool6,
shape=(tf.shape(self.pool6)[0],
-1)) # flatten
self.dense1 = ll.dense(self.flatten_layer, self.model_weights[18],
self.dropout_rate)
self.dense2 = ll.dense(self.dense1, self.model_weights[19],
self.dropout_rate)
self.dense3 = ll.dense(self.dense2, self.model_weights[20],
self.dropout_rate)
self.dense4 = ll.dense(self.dense3, self.model_weights[21],
self.dropout_rate)
self.dense5 = ll.dense(self.dense4, self.model_weights[22],
self.dropout_rate)
self.dense6 = tf.matmul(self.dense5, self.model_weights[23])
return tf.nn.softmax(self.dense6)
def __init__(self):
self._epochs = 20
self._learning_rate = 0.01
self._batch_size = 20
self._data = self.getData()
self._model = layers.Model(lr=self._learning_rate,
blr=self._learning_rate)
self._model.add_layer(
layers.conv(ems=1,
nodes=20,
kernel_size=3,
padding=1,
activation_function_="relu"))
self._model.add_layer(
layers.conv(ems=20,
nodes=20,
kernel_size=3,
padding=1,
activation_function_="relu"))
self._model.add_layer(layers.max_pool(kernel_size=2))
self._model.add_layer(
layers.conv(ems=20,
nodes=12,
kernel_size=3,
padding=1,
activation_function_="relu"))
self._model.add_layer(
layers.conv(ems=12,
nodes=12,
kernel_size=3,
padding=1,
activation_function_="relu"))
self._model.add_layer(layers.max_pool(kernel_size=2))
self._model.add_layer(
layers.conv(ems=12,
nodes=6,
kernel_size=3,
padding=1,
activation_function_="relu"))
self._model.add_layer(
layers.conv(ems=6,
nodes=6,
kernel_size=3,
padding=1,
activation_function_="relu"))
self._model.add_layer(layers.max_pool(kernel_size=2))
self._model.add_layer(layers.dense(eis=96, nodes=48, act_func="tanh"))
self._model.add_layer(layers.dense(eis=48, nodes=3, act_func="none"))
def add_prediction_op(self):
"""Applies a GRU RNN over the input data, then an affine layer projection. Steps to complete
in this function:
- Roll over inputs_placeholder with GRUCell, producing a Tensor of shape [batch_s, max_timestep,
hidden_size].
- Apply a W * f + b transformation over the data, where f is each hidden layer feature. This
should produce a Tensor of shape [batch_s, max_timesteps, num_classes]. Set this result to
"logits".
Remember:
* Use the xavier initialization for matrices (W, but not b).
* W should be shape [hidden_size, num_classes].
"""
# Non-recurrent hidden layers
inputs = self.inputs_placeholder
for i in range(self.config.num_hidden_layers):
with tf.variable_scope('hidden%d' % (i + 1)) as vs:
inputs = layers.dense(inputs=inputs,
output_size=self.config.hidden_size,
activation=tf.nn.relu)
# Construct forward and backward cells of bidirectional RNN
construct_cell = getattr(tf.contrib.rnn, self.config.cell_type)
fwdcell = construct_cell(
self.config.hidden_size,
activation=self.config.activation_func,
)
bckcell = construct_cell(
self.config.hidden_size,
activation=self.config.activation_func,
)
# TODO: look into non-zero initial hidden states?
rnn_outputs, rnn_last_states = tf.nn.bidirectional_dynamic_rnn(
fwdcell,
bckcell,
inputs=inputs,
dtype=tf.float32,
sequence_length=self.seq_lens_placeholder)
# Concatenate the forward and backward hidden states together for the scores
# scores.shape = [batch_s, max_timestep, 2*num_hidden]
scores = tf.concat([rnn_outputs[0], rnn_outputs[1]],
axis=2,
name='scores')
# Push the scores through an affine layer
# logits.shape = [batch_s, max_timestep, num_classes]
with tf.variable_scope('final') as vs:
self.logits = layers.dense(inputs=scores,
output_size=self.config.num_classes)
def _build(self):
layer = self.input_state
init_b = tf.constant_initializer(0.01)
for i, num_unit in enumerate(self.hidden_layers):
if i != 1:
layer = dense(layer, num_unit, init_b=init_b, name='hidden_layer_{}'.format(i))
else:
layer = tf.concat([layer, self.input_action], axis=1, name='concat_action')
layer = dense(layer, num_unit, init_b=init_b, name='hidden_layer_{}'.format(i))
output = dense(layer, 1, activation=None, init_b=init_b, name='output')
return tf.reshape(output, shape=(-1,))
def __call__(self, x, is_training=True, reuse=False, *args, **kwargs):
with tf.variable_scope(self.__class__.__name__) as vs:
if reuse:
vs.reuse_variables()
conv_params = {'is_training': is_training, 'activation_': 'relu'}
x = conv_block(x, 16, **conv_params)
x = conv_block(x, 16, **conv_params, sampling='pool')
x = conv_block(x, 32, **conv_params)
x = conv_block(x, 32, **conv_params, sampling='pool')
x = flatten(x)
x = dense(x, 512, activation_='relu')
x = dense(x, self.nb_classes)
return x
def encoder(current, reuse_variables=False):
"""
Creates encoder network.
@param current: tensor of size 96x96x3
@return: tensor of size config.num_z_channels
"""
if reuse_variables:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("encoder") as scope:
# -- transposed convolutional layer 1-4
for index, num_filters in enumerate([64, 128, 256, 512]):
name = 'E_conv' + str(index)
current = conv2d(current,
num_filters,
name=name,
reuse=reuse_variables)
current = tf.nn.relu(current)
# reshape
current = tf.reshape(current, [size_batch, -1])
# -- fc layer
name = 'E_fc'
current = dense(current,
num_z_channels,
name=name,
reuse=reuse_variables)
return tf.nn.tanh(current)
def build_dec(t_params, n_dim_txt, n_dim_enc, n_dim_dec, n_dim_vocab, beam_size):
'''
Build the decoder for texts
'''
def _step(_prob):
_y = _prob.argmax(-1)
_log_prob = tensor.log(_prob[tensor.arange(_y.shape[0]), _y] + 1e-6)
tensor.set_subtensor(_prob[tensor.arange(_y.shape[0]), _y], 0)
return _y, _log_prob
y = tensor.vector('y', 'int32')
init_h = tensor.matrix('init_h', config.floatX)
n_samples = y.shape[0]
# Word embedding
emb = tensor.switch(y[:, None] < 0, tensor.zeros((n_samples, n_dim_txt), config.floatX), embedding(y, t_params, n_dim_vocab, n_dim_txt, 'emb'))
# Decoder(s) - Initialization of hidden layer in the next step
next_h = gru(tensor.ones_like(y, 'int8'), emb, t_params, n_dim_txt, n_dim_dec, 'dec', True, init_h)
# Full-connected layer
fc = dense(0.5 * next_h, t_params, n_dim_dec, n_dim_vocab, 'fc')
# Classifier
prob = tensor.nnet.softmax(fc)
# Hypo words
[next_y, next_log_prob], _ = theano.scan(_step, non_sequences=prob, n_steps=beam_size)
return theano.function([y, init_h], [next_y, next_log_prob, next_h], name='f_dec')
def build_dec(t_params, n_dim_txt, n_dim_enc, n_dim_dec, n_dim_vocab,
beam_size):
'''
Build the decoder for texts
'''
def _step(_prob):
_y = _prob.argmax(-1)
_log_prob = tensor.log(_prob[tensor.arange(_y.shape[0]), _y] + 1e-6)
tensor.set_subtensor(_prob[tensor.arange(_y.shape[0]), _y], 0)
return _y, _log_prob
y = tensor.vector('y', 'int32')
init_h = tensor.matrix('init_h', config.floatX)
n_samples = y.shape[0]
# Word embedding
emb = tensor.switch(y[:, None] < 0,
tensor.zeros((n_samples, n_dim_txt), config.floatX),
embedding(y, t_params, n_dim_vocab, n_dim_txt, 'emb'))
# Decoder(s) - Initialization of hidden layer in the next step
next_h = gru(tensor.ones_like(y, 'int8'), emb, t_params, n_dim_txt,
n_dim_dec, 'dec', True, init_h)
# Full-connected layer
fc = dense(0.5 * next_h, t_params, n_dim_dec, n_dim_vocab, 'fc')
# Classifier
prob = tensor.nnet.softmax(fc)
# Hypo words
[next_y, next_log_prob], _ = theano.scan(_step,
non_sequences=prob,
n_steps=beam_size)
return theano.function([y, init_h], [next_y, next_log_prob, next_h],
name='f_dec')
def __call__(self, x, reuse=True, is_feature=False, is_training=True):
nb_downsampling = int(np.log2(self.input_shape[0] // 4))
with tf.variable_scope(self.name, reuse=reuse) as vs:
if reuse:
vs.reuse_variables()
_x = x
first_filters = 32
for i in range(nb_downsampling):
filters = first_filters * (2**i)
_x = conv_block(_x,
is_training=is_training,
filters=filters,
activation_='lrelu',
sampling='down',
normalization=self.normalization)
_x = flatten(_x)
if self.normalization == 'spectral':
_x = sn_dense(_x,
is_training=is_training,
units=1,
activation_=None)
else:
_x = dense(_x, units=1, activation_=None)
return _x
def first_block(x,
target_size,
noise_dim,
upsampling='deconv',
normalization='batch',
is_training=True):
if upsampling == 'deconv':
_x = reshape(x, (1, 1, noise_dim))
_x = conv2d_transpose(_x,
1024,
target_size,
strides=(1, 1),
padding='valid')
elif upsampling == 'dense':
_x = dense(x, target_size[0] * target_size[1] * 1024)
_x = reshape(_x, (target_size[1], target_size[0], 1024))
else:
raise ValueError
if normalization == 'batch':
_x = batch_norm(_x, is_training=is_training)
elif normalization == 'layer':
_x = layer_norm(_x, is_training=is_training)
elif normalization is None:
pass
else:
raise ValueError
_x = activation(_x, 'relu')
return _x
def discriminator(self, img, const_init=False, trainable=True, reuse=False):
# (n, 1, 28, 28)
h0 = layers.conv2d(
img,
64,
5,
name="d_conv1",
const_init=const_init,
trainable=trainable,
reuse=reuse,
)
h0 = flow.nn.leaky_relu(h0, 0.3)
h0 = flow.nn.dropout(h0, rate=0.3)
# (n, 64, 14, 14)
h1 = layers.conv2d(
h0,
128,
5,
name="d_conv2",
const_init=const_init,
trainable=trainable,
reuse=reuse,
)
h1 = flow.nn.leaky_relu(h1, 0.3)
h1 = flow.nn.dropout(h1, rate=0.3)
# (n, 128 * 7 * 7)
out = flow.reshape(h1, (self.batch_size, -1))
# (n, 1)
out = layers.dense(
out, 1, name="d_fc", const_init=const_init, trainable=trainable, reuse=reuse
)
return out
def get_opt_dict(self):
x = tf.placeholder(tf.float32, shape=[None, self.hw, self.hw])
y = tf.placeholder(tf.float32, shape=[1, ])
is_training = tf.placeholder(tf.bool, shape=[])
embedding = self.encoder(tf.expand_dims(x,-1))
attn = None
if self.use_attn:
embedding, attn = self.attention(embedding)
else:
embedding = tf.reduce_mean(
tf.expand_dims(embedding, axis=0), axis=1)
logits = dense(embedding, 1)
logits = tf.reshape(logits, [-1])
loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits))
var_total = tf.trainable_variables()
decay_var = [v for v in var_total if 'kernel' in v.name]
optimizer = tf.contrib.opt.AdamWOptimizer(
weight_decay=self.wd, learning_rate=self.lr)
train_op = optimizer.minimize(loss=loss, var_list=var_total,
decay_var_list=decay_var)
return EasyDict(
x=x, y=y, is_training=is_training, attn=attn,
logits=logits, loss=loss, train_op=train_op)
def discriminator_img(current, valence, arousal, reuse_variables=False):
"""
Creates discriminator network on generated image + desired emotion.
@param current: tensor of size 96x96x3
@param valence: tensor of size 1
@param arousal: tensor of size 1
@return: sigmoid(output), output
(output tensor is of size 1)
"""
if reuse_variables:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("discriminator_img") as scope:
# -- convolutional blocks (= convolution+batch_norm+relu) 1-4
for index, num_filters in enumerate([16, 32, 64, 128]):
# convolution
name = 'D_img_conv' + str(index + 1)
current = conv2d(current,
num_filters,
name=name,
reuse=reuse_variables)
# batch normalization
name = 'D_img_bn' + str(index + 1)
current = batch_norm(current, name, reuse=reuse_variables)
# relu activation
current = tf.nn.relu(current)
if index == 0:
current = concat_label(current, valence, 16)
current = concat_label(current, arousal, 16)
# reshape
current = tf.reshape(current, [size_batch, -1])
# -- fc 1
name = 'D_img_fc1'
current = lrelu(dense(current, 1024, name=name, reuse=reuse_variables))
# -- fc 2
name = 'D_img_fc2'
current = dense(current, 1, name=name, reuse=reuse_variables)
return tf.nn.sigmoid(current), current
def _build(self):
layer = self.x
init_b = tf.constant_initializer(0.01)
for i, num_unit in enumerate(self.hidden_layers):
layer = dense(layer,
num_unit,
init_b=init_b,
name='hidden_layer_{}'.format(i))
output = dense(layer,
self.output_dim,
activation=self.activation,
init_b=init_b,
name='output')
return output
def encoder(self, x):
out = conv2d(x, 20, 5, activation=tf.nn.relu)
out = max_pool(out, 2, 2)
out = conv2d(out, 50, 5, activation=tf.nn.relu)
out = max_pool(out, 2, 2)
out = tf.layers.flatten(out)
out = dense(out, 500, activation=tf.nn.relu)
return out
def __init__(self):
conv_stride = [4, 4]
self.conv1 = layers.conv2d(filters=96, kernel=[11, 11], padding='SAME', name='conv1', activation='relu', normalization='local_response_normalization', stride=conv_stride)
self.conv2 = layers.conv2d(filters=256, kernel=[5, 5], padding='SAME', name='conv2', activation='relu', normalization="local_response_normalization", stride=[1, 1])
self.conv1 = layers.conv2d(filters=96, kernel=[11, 11], padding='SAME', name='conv1', activation='relu', normalization='local_response_normalization', stride=conv_stride)
self.conv2 = layers.conv2d(filters=256, kernel=[5, 5], padding='SAME', name='conv2', activation='relu', normalization="local_response_normalization", stride=[1, 1])
self.conv3 = layers.conv2d(filters=384, kernel=[3, 3], padding='SAME', name='conv3', activation='relu', stride=[1, 1])
self.conv4 = layers.conv2d(filters=384, kernel=[3, 3], padding='SAME', name='conv4', activation='relu', stride=[1, 1])
self.conv5 = layers.conv2d(filters=256, kernel=[3, 3], padding='SAME', name='conv5', activation='relu', stride=[1, 1])
self.fc6 = layers.dense(4096, activation='relu', dropout=True, name='fc6')
self.fc7 = layers.dense(4096, activation='relu', dropout=True, name='fc7')
self.fc8 = layers.dense(1000, activation='relu', name='fc8')
self.max_pool1 = layers.max_pool2d(ksize=[3, 3], stride=[2, 2])
self.max_pool2 = layers.max_pool2d(ksize=[3, 3], stride=[2, 2])
self.max_pool5 = layers.max_pool2d(ksize=[3, 3], stride=[2, 2])
def build(self, input_shape=(28, 28, 1), classes=10):
inputs = keras.Input(shape=input_shape)
outputs = flatten()(inputs)
outputs = dense(300)(outputs)
outputs = sigmoid()(outputs)
outputs = dense(100)(outputs)
outputs = sigmoid()(outputs)
outputs = dense(10)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model
def classifier_aux(self, inputs, classes):
filters = 128
outputs = average_pooling2d(pool_size=(5, 5),
strides=3,
padding='valid')(inputs)
outputs = conv2d(filters=filters,
kernel_size=(1, 1),
strides=1,
padding='same')(outputs)
outputs = flatten()(outputs)
outputs = relu()(outputs)
outputs = dense(1024)(outputs)
outputs = relu()(outputs)
outputs = dropout(0.7)(outputs)
outputs = dense(classes)(outputs)
outputs = softmax()(outputs)
return outputs
def __call__(self, x, reuse=True, is_feature=False):
with tf.variable_scope(self.name) as vs:
if reuse:
vs.reuse_variables()
x = conv_block(x,
32,
kernel_size=(4, 4),
sampling='down',
**self.conv_kwargs)
for i in range(2):
x = discriminator_block(x, 32, **self.conv_kwargs)
x = conv_block(x,
64,
kernel_size=(4, 4),
sampling='down',
**self.conv_kwargs)
for i in range(4):
x = discriminator_block(x, 64, **self.conv_kwargs)
x = conv_block(x,
128,
kernel_size=(4, 4),
sampling='down',
**self.conv_kwargs)
for i in range(4):
x = discriminator_block(x, 128, **self.conv_kwargs)
x = conv_block(x,
256,
kernel_size=(4, 4),
sampling='down',
**self.conv_kwargs)
for i in range(4):
x = discriminator_block(x, 256, **self.conv_kwargs)
x = conv_block(x,
512,
kernel_size=(4, 4),
sampling='down',
**self.conv_kwargs)
for i in range(4):
x = discriminator_block(x, 512, **self.conv_kwargs)
x = conv_block(x,
1024,
kernel_size=(4, 4),
sampling='down',
**self.conv_kwargs)
if is_feature:
return x
x = global_average_pool2d(x)
x = dense(x, units=1, activation_=None)
return x
def __call__(self, x,
is_training=True,
reuse=False,
*args,
**kwargs):
with tf.variable_scope(self.__class__.__name__) as vs:
if reuse:
vs.reuse_variables()
conv_params = {'is_training': is_training,
'activation_': 'relu',
'normalization': 'batch'}
x = conv_block(x, 64, **conv_params, dropout_rate=0.3)
x = conv_block(x, 64, **conv_params, dropout_rate=0.3)
x = conv_block(x, 128, **conv_params, dropout_rate=0.4)
x = conv_block(x, 128, **conv_params, dropout_rate=0.4)
x = conv_block(x, 256, **conv_params, dropout_rate=0.4)
x = conv_block(x, 256, **conv_params, dropout_rate=0.4)
x = conv_block(x, 256, **conv_params)
l1 = x
x = max_pool2d(x)
x = conv_block(x, 512, **conv_params, dropout_rate=0.4)
x = conv_block(x, 512, **conv_params, dropout_rate=0.4)
x = conv_block(x, 512, **conv_params)
l2 = x
x = max_pool2d(x)
x = conv_block(x, 512, **conv_params, dropout_rate=0.4)
x = conv_block(x, 512, **conv_params, dropout_rate=0.4)
x = conv_block(x, 512, **conv_params)
l3 = x
x = max_pool2d(x)
x = conv_block(x, 512, **conv_params, sampling='pool')
x = conv_block(x, 512, **conv_params, sampling='pool')
x = flatten(x)
g = dense(x, 512, activation_='relu')
x, attentions = attention_module([l1, l2, l3], g)
x = dense(x, self.nb_classes)
return x, attentions
def classifier_main(self, inputs, classes):
outputs = average_pooling2d(pool_size=(7, 7),
strides=1,
padding='valid')(inputs)
outputs = flatten()(outputs)
outputs = dropout(0.4)(outputs)
outputs = dense(classes)(outputs)
outputs = softmax()(outputs)
return outputs
def build_enc(t_params, n_dim_img, n_dim_enc, n_dim_dec):
'''
Build the encoder for images
'''
x = tensor.tensor3('x', config.floatX)
mask_x = tensor.matrix('mask_x', 'int8')
# Encoder(s) and initialization of hidden layer
enc = gru(mask_x, 0.5 * x, t_params, n_dim_img, n_dim_enc, 'enc')[-1]
init_h = tensor.tanh(dense(enc, t_params, n_dim_enc, n_dim_dec, 'init_h'))
return theano.function([x, mask_x], [init_h], name='f_enc')
def build_dec(t_params, n_dim_txt, n_dim_enc, n_dim_dec, n_dim_vocab):
'''
Build the decoder for texts
'''
y = tensor.vector('y', 'int32')
prev_h = tensor.matrix('init_h', config.floatX)
n_samples = y.shape[0]
# Word embedding
emb = tensor.switch(y[:, None] < 0, tensor.zeros((n_samples, n_dim_txt), config.floatX), embedding(y, t_params, n_dim_vocab, n_dim_txt, 'emb'))
# Decoder(s) - Initialization of hidden layer in the next step
next_h = gru(tensor.ones_like(y, 'int8'), emb, t_params, n_dim_txt, n_dim_dec, 'dec', True, prev_h)
# Full-connected layer
fc = dense(0.5 * next_h, t_params, n_dim_dec, n_dim_vocab, 'fc')
# Classifier
prob = tensor.nnet.softmax(fc)
return theano.function([y, prev_h], [prob.argmax(-1), next_h], name='f_dec')
