def discriminator_stego_nn(self, img, reuse=False):
with tf.variable_scope('S_network'):
if reuse:
tf.get_variable_scope().reuse_variables()
net = img
net = self.image_processing_layer(img)
net = self.batch_norm(net, scope='d_s_bn0')
net = conv2d(net, self.df_dim, kernel_size=[5, 5], stride=[2, 2],
activation_fn=self.leaky_relu, scope='d_s_h0_conv')
net = self.batch_norm(net, scope='d_s_bn1')
net = conv2d(net, self.df_dim * 2, kernel_size=[5, 5], stride=[2, 2],
activation_fn=self.leaky_relu, scope='d_s_h1_conv')
net = self.batch_norm(net, scope='d_s_bn2')
net = conv2d(net, self.df_dim * 4, kernel_size=[5, 5], stride=[2, 2],
activation_fn=self.leaky_relu, scope='d_s_h2_conv')
net = self.batch_norm(net, scope='d_s_bn3')
net = conv2d(net, self.df_dim * 8, kernel_size=[5, 5], stride=[2, 2],
activation_fn=self.leaky_relu, scope='d_s_h3_conv')
net = self.batch_norm(net, scope='d_s_bn4')
net = tf.reshape(net, [self.conf.batch_size, -1])
net = linear(net, 1, activation_fn=tf.nn.sigmoid, scope='d_s_h4_lin',
weights_initializer=tf.random_normal_initializer(stddev=0.02))
return net
def generator_nn(self, noise, train=True):
with tf.variable_scope('G_network'):
if not train:
tf.get_variable_scope().reuse_variables()
gen = linear(noise, self.gf_dim * 8 * 4 * 4, scope='g_h0_lin',
activation_fn=None, weights_initializer=tf.random_normal_initializer(stddev=0.02))
gen = tf.reshape(gen, [-1, 4, 4, self.gf_dim * 8])
# gen = self.batch_norm(gen, reuse=(not train), scope='g_bn0')
gen = self.g_bn0(gen, train=train)
gen = tf.nn.relu(gen)
gen = self.conv2d_transpose(gen, [self.conf.batch_size, 8, 8, self.gf_dim * 4], name='g_h1')
# gen = self.batch_norm(gen, reuse=(not train), scope='g_bn1')
gen = self.g_bn1(gen, train=train)
gen = tf.nn.relu(gen)
gen = self.conv2d_transpose(gen, [self.conf.batch_size, 16, 16, self.gf_dim * 2], name='g_h2')
# gen = self.batch_norm(gen, reuse=(not train), scope='g_bn2')
gen = self.g_bn2(gen, train=train)
gen = tf.nn.relu(gen)
gen = self.conv2d_transpose(gen, [self.conf.batch_size, 32, 32, self.gf_dim * 1], name='g_h3')
# gen = self.batch_norm(gen, reuse=(not train), scope='g_bn3')
gen = self.g_bn3(gen, train=train)
gen = tf.nn.relu(gen)
out = self.conv2d_transpose(gen, [self.conf.batch_size, 64, 64, self.c_dim], name='g_out')
return tf.nn.tanh(out)
def softmax_model(X, Y_, mode):
Ylogits = layers.linear(X, 10)
predict = tf.nn.softmax(Ylogits)
classes = tf.cast(tf.argmax(predict, 1), tf.uint8)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(Ylogits, tf.one_hot(Y_, 10)))*100
train_op = layers.optimize_loss(loss, framework.get_global_step(), 0.003, "Adam")
return {"predictions":predict, "classes": classes}, loss, train_op
def conv_model(X, Y_, mode):
XX = tf.reshape(X, [-1, 28, 28, 1])
biasInit = tf.constant_initializer(0.1, dtype=tf.float32)
Y1 = layers.conv2d(XX, num_outputs=6, kernel_size=[6, 6], biases_initializer=biasInit)
Y2 = layers.conv2d(Y1, num_outputs=12, kernel_size=[5, 5], stride=2, biases_initializer=biasInit)
Y3 = layers.conv2d(Y2, num_outputs=24, kernel_size=[4, 4], stride=2, biases_initializer=biasInit)
Y4 = layers.flatten(Y3)
Y5 = layers.relu(Y4, 200, biases_initializer=biasInit)
# to deactivate dropout on the dense layer, set keep_prob=1
Y5d = layers.dropout(Y5, keep_prob=0.75, noise_shape=None, is_training=mode==learn.ModeKeys.TRAIN)
Ylogits = layers.linear(Y5d, 10)
predict = tf.nn.softmax(Ylogits)
classes = tf.cast(tf.argmax(predict, 1), tf.uint8)
loss = conv_model_loss(Ylogits, Y_, mode)
train_op = conv_model_train_op(loss, mode)
eval_metrics = conv_model_eval_metrics(classes, Y_, mode)
return learn.ModelFnOps(
mode=mode,
# You can name the fields of your predictions dictionary as you like.
predictions={"predictions": predict, "classes": classes},
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics
)
def discriminator_2layer(H, opt, dropout, prefix='', num_outputs=1, is_reuse=None):
# last layer must be linear
# H = tf.squeeze(H, [1,2])
# pdb.set_trace()
biasInit = tf.constant_initializer(0.001, dtype=tf.float32)
H_dis = layers.fully_connected(tf.nn.dropout(H, keep_prob=dropout), num_outputs=opt.H_dis,
biases_initializer=biasInit, activation_fn=tf.nn.relu, scope=prefix + 'dis_1',
reuse=is_reuse)
logits = layers.linear(tf.nn.dropout(H_dis, keep_prob=dropout), num_outputs=num_outputs,
biases_initializer=biasInit, scope=prefix + 'dis_2', reuse=is_reuse)
return logits
def network(self):
net = self.images
net = self.image_processing_layer(net)
def get_init():
return tf.truncated_normal_initializer(stddev=0.02)
net = conv2d(net, 10, [7, 7], activation_fn=tf.nn.relu, name='conv1', weights_initializer=get_init())
net = conv2d(net, 20, [5, 5], activation_fn=tf.nn.relu, name='conv2', weights_initializer=get_init())
net = tf.nn.max_pool(net, [1, 4, 4, 1], [1, 1, 1, 1], padding='SAME')
net = conv2d(net, 30, [3, 3], activation_fn=tf.nn.relu, name='conv3', weights_initializer=get_init())
net = conv2d(net, 40, [3, 3], activation_fn=tf.nn.relu, name='conv4', weights_initializer=get_init())
net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 1, 1, 1], padding='SAME')
net = tf.reshape(net, [self.conf.batch_size, -1])
net = linear(net, 100, activation_fn=tf.nn.tanh, name='FC1')
out = linear(net, 2, activation_fn=tf.nn.softmax, name='out')
return out
def _logistic_regression_model_fn(features, labels, mode):
_ = mode
logits = layers.linear(
features,
1,
weights_initializer=init_ops.zeros_initializer(),
# Intentionally uses really awful initial values so that
# AUC/precision/recall/etc will change meaningfully even on a toy dataset.
biases_initializer=init_ops.constant_initializer(-10.0))
predictions = math_ops.sigmoid(logits)
loss = loss_ops.sigmoid_cross_entropy(logits, labels)
train_op = optimizers.optimize_loss(
loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
return predictions, loss, train_op
def conv_model(X, Y_):
XX = tf.reshape(X, [-1, 28, 28, 1])
Y1 = layers.conv2d(XX, num_outputs=6, kernel_size=[6, 6])
Y2 = layers.conv2d(Y1, num_outputs=12, kernel_size=[5, 5], stride=2)
Y3 = layers.conv2d(Y2, num_outputs=24, kernel_size=[4, 4], stride=2)
Y4 = layers.flatten(Y3)
Y5 = layers.relu(Y4, 200)
Ylogits = layers.linear(Y5, 10)
predict = tf.nn.softmax(Ylogits)
classes = tf.cast(tf.argmax(predict, 1), tf.uint8)
loss = tf.nn.softmax_cross_entropy_with_logits(Ylogits, tf.one_hot(Y_, 10))
train_op = layers.optimize_loss(loss, framework.get_global_step(), 0.003, "Adam")
return {"predictions":predict, "classes": classes}, loss, train_op
def conv_model(X, Y_, mode):
XX = tf.reshape(X, [-1, 28, 28, 1])
biasInit = tf.constant_initializer(0.1, dtype=tf.float32)
Y1 = layers.conv2d(XX, num_outputs=6, kernel_size=[6, 6], biases_initializer=biasInit)
Y2 = layers.conv2d(Y1, num_outputs=12, kernel_size=[5, 5], stride=2, biases_initializer=biasInit)
Y3 = layers.conv2d(Y2, num_outputs=24, kernel_size=[4, 4], stride=2, biases_initializer=biasInit)
Y4 = layers.flatten(Y3)
Y5 = layers.relu(Y4, 200, biases_initializer=biasInit)
Ylogits = layers.linear(Y5, 10)
predict = tf.nn.softmax(Ylogits)
classes = tf.cast(tf.argmax(predict, 1), tf.uint8)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(Ylogits, tf.one_hot(Y_, 10)))*100
train_op = layers.optimize_loss(loss, framework.get_global_step(), 0.001, "Adam")
return {"predictions":predict, "classes": classes}, loss, train_op
# Define the zero state of the cell
initial_state = cell.zero_state(mini_batch_size, tf.float32)
# Launch dynamic RNN Network with specified cell and initial state
# We use time_major=False because we would need to transpose the input on our own otherwise
rnn_outputs, _rnn_states = tf.nn.dynamic_rnn(cell, x,
initial_state=initial_state, time_major=False)
# Get the last $eval_time_steps timestep(s) for training
rnn_outputs_on_last_t_step = tf.slice(
rnn_outputs,
[0, n_in_time_steps - (1+eval_time_steps), 0],
[mini_batch_size, eval_time_steps, n_units])
# Project output from rnn output size to n_output
final_projection = lambda z: layers.linear(z, num_outputs=n_output,
activation_fn=tf.nn.sigmoid)
# Apply projection to every time step
predicted = tf.map_fn(final_projection, rnn_outputs_on_last_t_step)
# Error and backprop
error = tf.nn.l2_loss(tf.subtract(tf.abs(y),tf.abs(predicted)))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(error)
# Prediction error and accuracy
accuracy = tf.reduce_mean(tf.subtract(tf.abs(y),tf.abs(predicted)))
#-------------------------------------------------------------------------------
# RUN THE NETWORK
#-------------------------------------------------------------------------------
def generator(x, hidden_size):
with tf.variable_scope('Generator'):
h0 = tf.nn.softplus(layers.linear(x, hidden_size))
return layers.linear(h0, 1)
def inference_net(x, latent_size):
return layers.linear(x, latent_size)
# naive dropout
dropcells = [rnn.DropoutWrapper(each,input_keep_prob=pkeep) for each in cells]
multicell = rnn.MultiRNNCell(dropcells, state_is_tuple=False)
multicell = rnn.DropoutWrapper(multicell, output_keep_prob=pkeep) # dropout for the softmax layer
Yr, H = tf.nn.dynamic_rnn(multicell, Xo, dtype=tf.float32, initial_state=Hin)
H = tf.identity(H, name='H')
# Softmax layer implementation:
# Flatten the first two dimension of the output [ BATCHSIZE, SEQLEN, ALPHASIZE ] => [ BATCHSIZE x SEQLEN, ALPHASIZE ]
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
# From the readout point of view, a value coming from a sequence time step or a minibatch item is the same thing.
Y_flat = tf.reshape(Yr, [-1, INTERNALSIZE]) # [ BATCHSIZE x SEQLEN, INTERNALSIZE ]
Ylogits = layers.linear(Y_flat, ALPHASIZE) # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
Y_flat_ = tf.reshape(Yo_, [-1, ALPHASIZE]) # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_flat_) # [ BATCHSIZE x SEQLEN ]
loss = tf.reshape(loss, [batchsize, -1]) # [ BATCHSIZE, SEQLEN ]
Yo = tf.nn.softmax(Ylogits, name='Yo') # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
Y = tf.argmax(Yo, 1) # [ BATCHSIZE x SEQLEN ]
Y = tf.reshape(Y, [batchsize, -1], name="Y") # [ BATCHSIZE, SEQLEN ]
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
# stats for display
seqloss = tf.reduce_mean(loss, 1)
batchloss = tf.reduce_mean(seqloss)
accuracy = tf.reduce_mean(tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
loss_summary = tf.summary.scalar("batch_loss", batchloss)
accuracy_summary = tf.summary.scalar("batch_accuracy", accuracy)
summaries = tf.summary.merge([loss_summary, accuracy_summary])
def _build_network(self, input_width):
with self.session.graph.as_default():
if self.rnn_cell_type == "LSTM":
self.rnn_cell = tf.contrib.rnn.LSTMCell(self.rnn_cell_dim)
elif self.rnn_cell_type == "GRU":
self.rnn_cell = tf.contrib.rnn.GRUCell(self.rnn_cell_dim)
else:
raise ValueError("Unknown rnn_cell {}".format(rnn_cell))
self.global_step = tf.Variable(0,
dtype=tf.int64,
trainable=False,
name='global_step')
self.tokens = tf.placeholder(tf.int32, [None, None, None],
name="tokens")
self.token_lens = tf.placeholder(tf.int32, [None, None],
name="token_lens")
self.features = tf.placeholder(tf.float32, [None, None],
name="features")
self.labels = tf.placeholder(tf.int64, [None], name="labels")
self.alphabet_size = len(self.char_vocabulary.classes_)
self.dropout_keep = tf.placeholder(tf.float32)
self.input_width = input_width
char_embedding_matrix = tf.get_variable(
"char_embeddings", [self.alphabet_size, self.EMBEDDING_SIZE],
initializer=tf.random_normal_initializer(stddev=0.01),
dtype=tf.float32)
with tf.variable_scope("token_encoder"):
tokens_flat = tf.reshape(self.tokens,
[-1, tf.shape(self.tokens)[-1]])
token_lens_flat = tf.reshape(self.token_lens, [-1])
char_embeddings = tf.nn.embedding_lookup(
char_embedding_matrix, tokens_flat)
hidden_states, final_states = tf.nn.bidirectional_dynamic_rnn(
cell_fw=self.rnn_cell,
cell_bw=self.rnn_cell,
inputs=char_embeddings,
sequence_length=token_lens_flat,
dtype=tf.float32,
scope="char_BiRNN")
tokens_encoded = tf_layers.linear(tf.concat(final_states, 1),
self.EMBEDDING_SIZE,
scope="tokens_encoded")
tokens_encoded = tf.reshape(tokens_encoded,
[tf.shape(self.features)[0], -1])
self.input_layer = tf.concat((tokens_encoded, self.features), 1)
self.input_layer = tf.reshape(self.input_layer,
[-1, self.input_width])
# input transform
self.hidden_layer = tf.nn.dropout(
tf_layers.fully_connected(self.input_layer,
num_outputs=self.h_width,
activation_fn=None,
scope="input_layer"),
self.dropout_keep)
# hidden layers
for i in range(self.h_depth):
if self.layer_type == "FeedForward":
self.hidden_layer = tf.nn.dropout(
tf_layers.fully_connected(
self.hidden_layer,
num_outputs=self.h_width,
activation_fn=tf.nn.relu,
scope="ff_layer_{}".format(i)), self.dropout_keep)
elif self.layer_type == "Highway":
self.hidden_layer = tf.nn.dropout(
highway_layer(self.hidden_layer,
num_outputs=self.h_width,
activation_fn=tf.nn.relu,
scope="highway_layer_{}".format(i)),
self.dropout_keep)
else:
raise ValueError("Unknown hidden layer type.")
self.output_layer = tf_layers.fully_connected(
self.hidden_layer,
num_outputs=len(self.target_encoder.classes_),
activation_fn=None,
scope="output_layer")
self.predictions = tf.argmax(self.output_layer, 1)
self.loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.output_layer, labels=self.labels),
name="loss")
self.training = tf.train.AdamOptimizer().minimize(
self.loss, global_step=self.global_step)
self.accuracy = tf_metrics.accuracy(self.predictions, self.labels)
self.summary = tf.summary.merge([
tf.summary.scalar("train/loss", self.loss),
tf.summary.scalar("train/accuracy", self.accuracy)
])
self._initialize_variables()
def __init_decoder(self):
'''Initializes the decoder part of the model.'''
with tf.variable_scope('decoder') as scope:
output_fn = lambda outs: layers.linear(
outs, self.__get_vocab_size(), scope=scope)
if self.cfg.get('use_attention'):
attention_states = tf.transpose(self.encoder_outputs,
[1, 0, 2])
(attention_keys, attention_values, attention_score_fn,
attention_construct_fn) = seq2seq.prepare_attention(
attention_states=attention_states,
attention_option='bahdanau',
num_units=self.decoder_cell.output_size)
decoder_fn_train = seq2seq.attention_decoder_fn_train(
encoder_state=self.encoder_state,
attention_keys=attention_keys,
attention_values=attention_values,
attention_score_fn=attention_score_fn,
attention_construct_fn=attention_construct_fn,
name='attention_decoder')
decoder_fn_inference = seq2seq.attention_decoder_fn_inference(
output_fn=output_fn,
encoder_state=self.encoder_state,
attention_keys=attention_keys,
attention_values=attention_values,
attention_score_fn=attention_score_fn,
attention_construct_fn=attention_construct_fn,
embeddings=self.embeddings,
start_of_sequence_id=Config.EOS_WORD_IDX,
end_of_sequence_id=Config.EOS_WORD_IDX,
maximum_length=tf.reduce_max(self.encoder_inputs_length) +
3,
num_decoder_symbols=self.__get_vocab_size())
else:
decoder_fn_train = seq2seq.simple_decoder_fn_train(
encoder_state=self.encoder_state)
decoder_fn_inference = seq2seq.simple_decoder_fn_inference(
output_fn=output_fn,
encoder_state=self.encoder_state,
embeddings=self.embeddings,
start_of_sequence_id=Config.EOS_WORD_IDX,
end_of_sequence_id=Config.EOS_WORD_IDX,
maximum_length=tf.reduce_max(self.encoder_inputs_length) +
3,
num_decoder_symbols=self.__get_vocab_size())
(self.decoder_outputs_train, self.decoder_state_train,
self.decoder_context_state_train) = seq2seq.dynamic_rnn_decoder(
cell=self.decoder_cell,
decoder_fn=decoder_fn_train,
inputs=self.decoder_train_inputs_embedded,
sequence_length=self.decoder_train_length,
time_major=True,
scope=scope)
self.decoder_logits_train = output_fn(self.decoder_outputs_train)
self.decoder_prediction_train = tf.argmax(
self.decoder_logits_train,
axis=-1,
name='decoder_prediction_traion')
scope.reuse_variables()
(self.decoder_logits_inference, decoder_state_inference,
self.decoder_context_state_inference
) = seq2seq.dynamic_rnn_decoder(cell=self.decoder_cell,
decoder_fn=decoder_fn_inference,
time_major=True,
scope=scope)
self.decoder_prediction_inference = tf.argmax(
self.decoder_logits_inference,
axis=-1,
name='decoder_prediction_inference')
# to do for LSTM's tuple state, but can be achieved by creating two vector
# Variables, which are then tiled along batch dimension and grouped into tuple.
batch_size = tf.shape(inputs)[1]
initial_state = cell.zero_state(batch_size, tf.float32)
# Given inputs (time, batch, input_size) outputs a tuple
# - outputs: (time, batch, output_size) [do not mistake with OUTPUT_SIZE]
# - states: (time, batch, hidden_size)
rnn_outputs, rnn_states = tf.nn.dynamic_rnn(cell,
inputs,
initial_state=initial_state,
time_major=True)
# project output from rnn output size to OUTPUT_SIZE. Sometimes it is worth adding
# an extra layer here.
final_projection = lambda x: layers.linear(
x, num_outputs=OUTPUT_SIZE, activation_fn=tf.nn.sigmoid)
# apply projection to every timestep.
predicted_outputs = map_fn(final_projection, rnn_outputs)
# compute elementwise cross entropy.
error = -(outputs * tf.log(predicted_outputs + TINY) +
(1.0 - outputs) * tf.log(1.0 - predicted_outputs + TINY))
error = tf.reduce_mean(error)
# optimize
train_fn = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(error)
# assuming that absolute difference between output and correct answer is 0.5
# or less we can round it to the correct output.
accuracy = tf.reduce_mean(
def build(self):
hparam = self.hparam
if hparam.code_ndim == 3:
code_shape = [None, hparam.timesteps, hparam.code_dim]
cond_shape = [None, hparam.timesteps, hparam.cond_dim]
elif hparam.code_ndim == 2:
code_shape = [None, hparam.code_dim]
cond_shape = [None, hparam.cond_dim]
code = ori_code = tf.placeholder("float32", code_shape, name='code')
if hparam.conditional:
assert hparam.onehot is False, "NotImplemented: cond with onehot"
cond = tf.placeholder("float32", cond_shape, name='condition')
code = tf.concat([code, cond], -1)
real_seq = tf.placeholder("int32", [None, hparam.timesteps],
name='real_seq')
real_seq_img = tf.one_hot(real_seq, hparam.vocab_size)
dis_train = tf.placeholder('bool', name='is_train')
bs = tf.shape(ori_code)[0]
# generator
final_states = []
init_states = []
with tf.variable_scope('generator'):
# play with code
step = int(hparam.timesteps / np.prod(hparam.repeats))
first_input = code if hparam.code_ndim == 3 else \
tf.tile(tf.expand_dims(code, 1), (1, step, 1))
if hparam.timestep_pad:
first_input = tf.concat([
first_input,
tf.tile(
tf.expand_dims(
tf.expand_dims(tf.lin_space(0., 1., step), 0), -1),
(bs, 1, 1)),
], -1)
outputs = [first_input]
for ind in range(len(hparam.cells)):
repeat = hparam.repeats[ind]
cell_size = hparam.cells[ind]
bi = hparam.bidirection[ind]
with tf.variable_scope('layer{}'.format(ind)):
# if ind == len(hparam.repeats) and \
# hparam.last_bidirectional:
if bi:
# assert(repeat == 1)
fw_cell = hparam.basic_cell(cell_size)
bw_cell = hparam.basic_cell(cell_size)
fw_init = fw_cell.zero_state(bs, tf.float32)
bw_init = fw_cell.zero_state(bs, tf.float32)
output, state = tf.nn.bidirectional_dynamic_rnn(
fw_cell,
bw_cell,
outputs[-1],
initial_state_fw=fw_init,
initial_state_bw=bw_init,
dtype=tf.float32,
)
output = tf.concat(output, 2)
cell_size *= 2
init_states.extend([fw_init, bw_init])
final_states.append(state)
else:
cell = hparam.basic_cell(cell_size)
init = cell.zero_state(bs, tf.float32)
output, state = tf.nn.dynamic_rnn(
cell,
outputs[-1],
dtype=tf.float32,
)
init_states.append(init)
final_states.append(state)
# output = output * 2
if repeat != 1:
step *= repeat
output = tf.reshape(tf.tile(output, (1, 1, repeat)),
[bs, step, cell_size])
outputs.append(output)
outputs[-1] = outputs[-1][:, :hparam.timesteps, :]
for o in outputs:
print o
with tf.variable_scope('decision'):
if hparam.deconv_decision:
fake_seq_img = outputs[-1]
right = slim.fully_connected(fake_seq_img,
32 * 10,
activation_fn=None)
right = tf.reshape(right, [bs, hparam.timesteps, 10, 32])
# right = tf.nn.softmax(right, -1)
right = slim.conv2d_transpose(
right,
1, [1, 12],
stride=(1, 12),
activation_fn=tf.tanh)[:, :, :, 0]
fake_seq_img = right
fake_seq_img = tf.concat([
tf.ones([bs, hparam.timesteps, 4]) * -1, fake_seq_img,
tf.ones([bs, hparam.timesteps, 4]) * -1
],
axis=2)
print fake_seq_img
else:
fake_seq_img = outputs[-1]
fake_seq_img = layers.linear(fake_seq_img,
hparam.vocab_size)
outputs.append(fake_seq_img)
fake_seq_img = tf.tanh(fake_seq_img)
# fake_seq_img = tf.nn.softmax(fake_seq_img, -1)
outputs.append(fake_seq_img)
fake_seq = tf.argmax(fake_seq_img, -1)
if hparam.plus_code:
fake_seq_img = tf.clip_by_value(fake_seq_img + code, -1., +1.)
# discriminator
if hparam.rnn_dis:
def dis(seq_img, bn_scope, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
print 'dis'
slices = tf.unstack(seq_img, axis=1)
fw_cell = hparam.basic_cell(32)
# bw_cell = hparam.basic_cell(64)
x, state = tf.nn.static_rnn(
fw_cell, # bw_cell,
slices,
dtype=tf.float32,
)
x = tf.stack(x, axis=1)
print x
# x = tf.concat(x, 2)
x = slim.linear(x, 1)
print x
x = slim.flatten(x)
print x
x = slim.linear(x, 1)
print x
# x = tf.nn.sigmoid(x)
return x
else:
def dis(seq_img, bn_scope, reuse=False, cond_vec=None):
with tf.variable_scope('discriminator', reuse=reuse):
fs = 32
covariance = tf.matmul(seq_img, seq_img, transpose_b=True)
x = tf.expand_dims(covariance, -1)
x = lrelu(slim.conv2d(x, fs * 1, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
x = lrelu(slim.conv2d(x, fs * 2, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
x = lrelu(slim.conv2d(x, fs * 4, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
x = lrelu(slim.conv2d(x, fs * 4, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
covariance_feat = slim.flatten(x)
# x = tf.nn.embedding_lookup(embeddings, seq)
# x = ResNetBuilder(dis_train,
# bn_scopes=['fake', 'real'],
# bn_scope=bn_scope).\
# resnet(x, structure=[2, 2, 2, 2], filters=8, nb_class=1)
# note axis
fs = 32
x = seq_img
x = tf.expand_dims(seq_img, -1)
x = lrelu(slim.conv2d(x, fs * 1, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
x = lrelu(slim.conv2d(x, fs * 2, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
x = lrelu(slim.conv2d(x, fs * 4, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
x = lrelu(slim.conv2d(x, fs * 4, [5, 5]))
x = slim.max_pool2d(x, (2, 2))
seq_feat = slim.flatten(x)
feat = tf.concat([covariance_feat, seq_feat], axis=1)
if cond_vec is not None:
feat = tf.concat([feat, cond_vec], axis=-1)
feat = lrelu(slim.linear(feat, 200))
x = slim.linear(feat, 1)
# x = tf.nn.sigmoid(x)
return x
# opt
# problematic with the reuse bn
# fake_seq_img = tf.where(
# tf.greater(fake_seq_img, 0.5),
# fake_seq_img,
# tf.zeros_like(fake_seq_img))
if hparam.conditional:
if len(cond_shape) == 3:
raise Exception("NotImplemented: cond with ndim3 (DisNet)")
cond_real = tf.placeholder("float32", [None, hparam.cond_dim],
name='cond_real')
fake_dis_pred = dis(fake_seq_img,
cond_vec=cond if hparam.conditional else None,
bn_scope='fake')
real_dis_pred = dis(real_seq_img,
cond_vec=cond_real if hparam.conditional else None,
bn_scope='real',
reuse=True)
# traditional GAN loss
# G_loss = tf.reduce_mean(-safe_log(fake_dis_pred))
# D_loss = tf.reduce_mean(-safe_log(real_dis_pred)) +\
# tf.reduce_mean(-safe_log(1-fake_dis_pred))
# IWGAN
epsilon = tf.random_uniform(minval=0, maxval=1.0, shape=())
print 'grad'
intepolation = fake_seq_img * epsilon + real_seq_img * (1.0 - epsilon)
inte_dis_pred = dis(intepolation,
cond_vec=(cond * epsilon + cond_real *
(1.0 - epsilon)) /
2. if hparam.conditional else None,
bn_scope='intepolation',
reuse=True)
grad = tf.gradients(inte_dis_pred, intepolation)[0]
print grad
grad = tf.reshape(grad, (-1, hparam.timesteps * hparam.vocab_size))
print grad
D_loss = tf.reduce_mean(fake_dis_pred) - \
tf.reduce_mean(real_dis_pred) + \
10*tf.reduce_mean(tf.square(tf.norm(grad, ord=2, axis=1)-1))
G_loss = -tf.reduce_mean(fake_dis_pred)
print D_loss
print G_loss
fake_seq_img_grad = tf.gradients(G_loss, fake_seq_img)[0]
G_opt = tf.train.AdamOptimizer(learning_rate=hparam.G_lr,
beta1=0.5,
beta2=0.9)
# D_opt = tf.train.GradientDescentOptimizer(learning_rate=hparam.D_lr)
D_opt = tf.train.AdamOptimizer(learning_rate=hparam.D_lr,
beta1=0.5,
beta2=0.9)
D_iter = tf.Variable(0, name='D_iter')
G_iter = tf.Variable(0, name='G_iter')
trainable_gen_var = reduce(lambda x, y: x + y, [
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, ele)
for ele in hparam.trainable_gen
], [])
G_train_op = slim.learning.create_train_op(
G_loss,
G_opt,
variables_to_train=trainable_gen_var,
global_step=G_iter,
clip_gradient_norm=hparam.G_clipnorm)
D_train_op = slim.learning.create_train_op(
D_loss,
D_opt,
variables_to_train=tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, "discriminator"),
global_step=D_iter,
)
iter_step = tf.Variable(0, name='iter_step')
iter_step_op = iter_step.assign_add(1)
# input
self.ori_code = ori_code
self.code = code
self.real_seq = real_seq
self.real_seq_img = real_seq_img
if hparam.conditional:
self.cond = cond
self.cond_real = cond_real
# summary
self.summary_fake_img = tf.summary.image(
'fake_img', tf.expand_dims(fake_seq_img, -1))
self.summary_real_img = tf.summary.image(
'real_img', tf.expand_dims(real_seq_img, -1))
self.summary_G_loss = tf.summary.scalar('G_loss', G_loss)
self.summary_D_loss = tf.summary.scalar('D_loss', D_loss)
self.summary_fake_dis_pred = tf.summary.scalar(
'fake_dis_pred', tf.reduce_mean(fake_dis_pred))
self.summary_real_dis_pred = tf.summary.scalar(
'real_dis_pred', tf.reduce_mean(real_dis_pred))
self.summary_fake_img_grad = tf.summary.image(
'gradient_map', tf.expand_dims(fake_seq_img_grad, -1))
self.summary_first_input = tf.summary.image(
'noise', tf.expand_dims(first_input, -1))
self.gen_outputs = outputs
# debug
self.fake_seq_img = fake_seq_img
self.first_input = first_input
self.init_states = tuple(init_states)
self.final_states = tuple(final_states)
self.bs_tensor = bs
# train
self.dis_train = dis_train
self.G_train_op = G_train_op
self.D_train_op = D_train_op
self.iter_step = iter_step
self.iter_step_op = iter_step_op
# output
self.fake_seq = fake_seq
self.built = True
def train_rnn(args):
SEQLEN = 50
BATCHSIZE = args.batch_size
ALPHASIZE = txt.ALPHASIZE
INTERNALSIZE = 512
NLAYERS = 5
learning_rate = 0.0002 # small learning rate
dropout_keep = .9 # only some dropout they use .8 but .9 is my preference
text_files = args.data_path + '*.txt' # get all of the text files from data_path
codetext, valitext, bookranges = txt.read_data_files(text_files,
validation=True)
# set epoch size based on batchsize and sequence len
epoch_size = len(codetext) // (BATCHSIZE * SEQLEN)
# model placeholders
lr = tf.placeholder(tf.float32, name='learning_rate')
p_keep = tf.placeholder(tf.float32, name='p_keep')
batch_size = tf.placeholder(tf.int32, name='batch_size')
# input placeholders
X = tf.placeholder(tf.uint8, [None, None], name='X') #
Xo = tf.one_hot(X, ALPHASIZE, 1.0, 0.0)
# expected outputs = same sequence shifted by 1
Y_ = tf.placeholder(tf.uint8, [None, None], name='Y_')
Yo_ = tf.one_hot(Y_, ALPHASIZE, 1.0, 0.0)
# input state
Hin = tf.placeholder(tf.float32, [None, INTERNALSIZE * NLAYERS],
name='Hin')
# Using a NLAYERS=3 of cells, unrolled SEQLEN=30 times
# dynamic_rnn infers SEQLEN from the size of the inputs Xo
cells = [rnn.GRUCell(INTERNALSIZE) for _ in range(NLAYERS)]
# weird dropout, well simple dropout
drop_cells = [
rnn.DropoutWrapper(cell, input_keep_prob=p_keep) for cell in cells
]
multi_cell = rnn.MultiRNNCell(drop_cells, state_is_tuple=False)
multi_cell = rnn.DropoutWrapper(multi_cell, output_keep_prob=p_keep)
# ^ The last layer is for the softmax dropout
Yr, H = tf.nn.dynamic_rnn(multi_cell,
Xo,
dtype=tf.float32,
initial_state=Hin)
# H is that last state
H = tf.identity(H, name='H') # give it a tf name
# Softmax layer implementation:
# Flatten the first two dimension of the output [ BATCHSIZE, SEQLEN, ALPHASIZE ] => [ BATCHSIZE x SEQLEN, ALPHASIZE ]
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
# From the readout point of view, a value coming from a sequence time step or a minibatch item is the same thing.
Yflat = tf.reshape(Yr, [-1, INTERNALSIZE])
Ylogits = layers.linear(Yflat, ALPHASIZE)
Yflat_ = tf.reshape(Yo_, [-1, ALPHASIZE])
loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=Ylogits,
labels=Yflat_)
loss = tf.reshape(loss, [batch_size, -1])
Yo = tf.nn.softmax(Ylogits, name='Yo')
Y = tf.argmax(Yo, 1)
Y = tf.reshape(Y, [batch_size, -1], name="Y")
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
# stats for display
seqloss = tf.reduce_mean(loss, 1)
batchloss = tf.reduce_mean(seqloss)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
loss_summary = tf.summary.scalar("batch_loss", batchloss)
acc_summary = tf.summary.scalar("batch_accuracy", accuracy)
summaries = tf.summary.merge([loss_summary, acc_summary])
# Init Tensorboard stuff. This will save Tensorboard information into a different
# folder at each run named 'log/<timestamp>/'. Two sets of data are saved so that
# you can compare training and validation curves visually in Tensorboard.
timestamp = str(math.trunc(time.time()))
#summary_writer = tf.summary.FileWriter("log/" + timestamp + "-training")
#validation_writer = tf.summary.FileWriter("log/" + timestamp + "-validation")
# For saving models
os.makedirs(args.save_dir, exist_ok=True)
# Only the last checkpoint will be saved
saver = tf.train.Saver(max_to_keep=10)
gen_file = open(args.save_dir + 'generated.txt', 'w')
# For displaying progress
# - changing this to my own implementation
# - Theyres is too much output
# for display: init the progress bar
# TODO: Change this guy eventually
DISPLAY_FREQ = 50
_50_BATCHES = DISPLAY_FREQ * BATCHSIZE * SEQLEN
progress = txt.Progress(DISPLAY_FREQ,
size=111 + 2,
msg="Training on next " + str(DISPLAY_FREQ) +
" batches")
#
istate = np.zeros([BATCHSIZE, INTERNALSIZE * NLAYERS])
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
step = 0
# Training loop
for x, y_, epoch in txt.rnn_minibatch_sequencer(codetext,
BATCHSIZE,
SEQLEN,
nb_epochs=args.epochs):
# train on one minibatch
feed_dict = {
X: x,
Y_: y_,
Hin: istate,
lr: learning_rate,
p_keep: dropout_keep,
batch_size: BATCHSIZE
}
_, y, ostate = sess.run([train_step, Y, H], feed_dict=feed_dict)
# validation step
if step % _50_BATCHES == 0:
feed_dict = {
X: x,
Y_: y_,
Hin: istate,
p_keep: 1.0,
batch_size: BATCHSIZE
} # no dropout for validation
y, l, bl, acc, smm = sess.run(
[Y, seqloss, batchloss, accuracy, summaries],
feed_dict=feed_dict)
txt.print_learning_learned_comparison(x, y, l, bookranges, bl, acc,
epoch_size, step, epoch)
#summary_writer.add_summary(smm, step)
# run a validation step every 50 batches
# The validation text should be a single sequence but that's too slow (1s per 1024 chars!),
# so we cut it up and batch the pieces (slightly inaccurate)
# tested: validating with 5K sequences instead of 1K is only slightly more accurate, but a lot slower.
if step % _50_BATCHES == 0 and len(valitext) > 0:
VALI_SEQLEN = 1 * 1024 # Sequence length for validation. State will be wrong at the start of each sequence.
bsize = len(valitext) // VALI_SEQLEN
txt.print_validation_header(len(codetext), bookranges)
vali_x, vali_y, _ = next(
txt.rnn_minibatch_sequencer(valitext, bsize, VALI_SEQLEN,
1)) # all data in 1 batch
vali_nullstate = np.zeros([bsize, INTERNALSIZE * NLAYERS])
feed_dict = {
X: vali_x,
Y_: vali_y,
Hin: vali_nullstate,
p_keep: 1.0, # no dropout for validation
batch_size: bsize
}
ls, acc, smm = sess.run([batchloss, accuracy, summaries],
feed_dict=feed_dict)
txt.print_validation_stats(ls, acc)
# save validation data for Tensorboard
#validation_writer.add_summary(smm, step)
#saver.save(sess, '{}/rnn_val_save'.format(args.save_dir+'val/'), global_step=step)
# display a short text generated with the current weights and biases (every 150 batches)
if step // 3 % _50_BATCHES == 0:
txt.print_text_generation_header()
ry = np.array([[txt.convert_from_alphabet(ord("K"))]])
rh = np.zeros([1, INTERNALSIZE * NLAYERS])
gen_file.write(
'----------------- STEP {} -----------------\n'.format(step))
for k in range(1000):
ryo, rh = sess.run([Yo, H],
feed_dict={
X: ry,
p_keep: 1.0,
Hin: rh,
batch_size: 1
})
rc = txt.sample_from_probabilities(
ryo, topn=10 if epoch <= 1 else 2)
letter = (chr(txt.convert_to_alphabet(rc)))
gen_file.write(letter)
print(letter, end='')
ry = np.array([[rc]])
txt.print_text_generation_footer()
gen_file.write('\n')
# display progress bar
progress.step(reset=step % _50_BATCHES == 0)
# loop state around
istate = ostate
step += BATCHSIZE * SEQLEN
gen_file.close()
saved_file = saver.save(sess,
'{}rnn_{}'.format(args.save_dir, args.epochs),
global_step=step)
print("Saved file: " + saved_file)
def __init__(self, inputs_tf, dimo, dimz, dimg, dimu, max_u, o_stats,
g_stats, hidden, layers, env_name, **kwargs):
"""The discriminator network and related training code.
Args:
inputs_tf (dict of tensors): all necessary inputs for the network: the
observation (o), the goal (g), and the action (u)
dimo (int): the dimension of the observations
dimg (int): the dimension of the goals
dimu (int): the dimension of the actions
max_u (float): the maximum magnitude of actions; action outputs will be scaled
accordingly
o_stats (baselines.her.Normalizer): normalizer for observations
g_stats (baselines.her.Normalizer): normalizer for goals
hidden (int): number of hidden units that should be used in hidden layers
layers (int): number of hidden layers
"""
self.o_tf = tf.placeholder(tf.float32, shape=(None, self.dimo))
self.z_tf = tf.placeholder(tf.float32, shape=(None, self.dimz))
self.g_tf = tf.placeholder(tf.float32, shape=(None, self.dimg))
obs_tau_excludes_goal, obs_tau_achieved_goal = split_observation_tf(
self.env_name, self.o_tau_tf)
obs_excludes_goal, obs_achieved_goal = split_observation_tf(
self.env_name, self.o_tf)
# Discriminator networks
with tf.variable_scope('state_mi'):
# Mutual Information Neural Estimation
# shuffle and concatenate
x_in = obs_tau_excludes_goal
y_in = obs_tau_achieved_goal
y_in_tran = tf.transpose(y_in, perm=[1, 0, 2])
y_shuffle_tran = tf.random_shuffle(y_in_tran)
y_shuffle = tf.transpose(y_shuffle_tran, perm=[1, 0, 2])
x_conc = tf.concat([x_in, x_in], axis=-2)
y_conc = tf.concat([y_in, y_shuffle], axis=-2)
# propagate the forward pass
layerx = tf_layers.linear(x_conc, int(self.hidden / 2))
layery = tf_layers.linear(y_conc, int(self.hidden / 2))
layer2 = tf.nn.relu(layerx + layery)
output = tf_layers.linear(layer2, 1)
output = tf.nn.tanh(output)
# split in T_xy and T_x_y predictions
N_samples = tf.shape(x_in)[-2]
T_xy = output[:, :N_samples, :]
T_x_y = output[:, N_samples:, :]
# compute the negative loss (maximise loss == minimise -loss)
mean_exp_T_x_y = tf.reduce_mean(tf.math.exp(T_x_y), axis=-2)
neg_loss = -(tf.reduce_mean(T_xy, axis=-2) -
tf.math.log(mean_exp_T_x_y))
neg_loss = tf.check_numerics(neg_loss,
'check_numerics caught bad neg_loss')
self.mi_tf = neg_loss
with tf.variable_scope('skill_ds'):
self.logits_tf = nn(obs_achieved_goal,
[int(self.hidden / 2)] * self.layers +
[self.dimz])
self.sk_tf = tf.nn.softmax_cross_entropy_with_logits(
labels=self.z_tf, logits=self.logits_tf)
self.sk_r_tf = -1 * self.sk_tf
]
dropcells = [rnn.DropoutWrapper(cell, input_keep_prob=pkeep) for cell in cells]
stacked_cells = rnn.MultiRNNCell(dropcells, state_is_tuple=True)
stacked_cells = rnn.DropoutWrapper(stacked_cells, output_keep_prob=pkeep)
# Let dynamic_rnn do all the work
init_state = stacked_cells.zero_state(batchsize, tf.float32)
Y_out, last_state = tf.nn.dynamic_rnn(stacked_cells,
Xo,
dtype=tf.float32,
initial_state=init_state)
# Flatten and set up softmax layer
Y_flat = tf.reshape(Y_out,
[-1, state_size]) # batch_size*time_steps,state_size
Ylogits = layers.linear(Y_flat, NUM_CHARS) # batch_size*time_steps,NUM_CHARS
Yflat_ = tf.reshape(Yo_, [-1, NUM_CHARS]) # batch_size*time_steps,NUM_CHARS
Yo = tf.nn.softmax(Ylogits) # batch_size*time_steps,NUM_CHARS
Y = tf.argmax(Yo, 1) # batch_size*time_steps
Y = tf.reshape(Y, [batchsize, -1]) # batch_size,time_steps
# Define our loss function
loss = tf.nn.softmax_cross_entropy_with_logits(
logits=Ylogits, labels=Yflat_) # batch_size*time_steps
loss = tf.reshape(loss, [batchsize, -1]) # batch_size,time_steps
# Define the training step using AdamOptimizer
train_step = tf.train.AdamOptimizer(learn_rate).minimize(loss)
# Define saver to create checkpoints during training
if not os.path.exists("checkpoints"):
def discriminator_0layer(H, opt, dropout, prefix='', num_outputs=1, is_reuse=None): H = tf.squeeze(H) biasInit = tf.constant_initializer(0.001, dtype=tf.float32) logits = layers.linear(tf.nn.dropout(H, keep_prob=dropout), num_outputs=num_outputs, biases_initializer=biasInit, scope=prefix + 'dis', reuse=is_reuse) return logits
if args.cell_type == 1:
net = [rnn.BasicLSTMCell(args.internal_size, state_is_tuple=False) for _ in range(args.layers)]
else:
net = [rnn.GRUCell(args.internal_size) for _ in range(args.layers)]
net = [rnn.DropoutWrapper(cell, input_keep_prob=dropout_prob) for cell in net]
multi_rnn = rnn.MultiRNNCell(net, state_is_tuple=False)
drop_multi_rnn = rnn.DropoutWrapper(multi_rnn, output_keep_prob=dropout_prob)
Yr, H = tf.nn.dynamic_rnn(drop_multi_rnn, Xo, initial_state=initial_state, dtype=tf.float32)
H = tf.identity(H, name="H")
Yflat = tf.reshape(Yr, [-1, args.internal_size])
Ylogits = layers.linear(Yflat, VOCAB_SIZE)
Yflat_ = tf.reshape(Yo_, [-1, VOCAB_SIZE])
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits,
labels=Yflat_)
loss = tf.reshape(loss, [batchsize, -1])
Yo = tf.nn.softmax(Ylogits, name="Yo")
Y = tf.argmax(Yo, 1)
Y = tf.reshape(Y, [batchsize, -1], name="Y")
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
# stats for display
def test_dynamic_rnn_decoder_time_major(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)) as varscope:
# Define inputs/outputs to model
batch_size = 2
encoder_embedding_size = 3
decoder_embedding_size = 4
encoder_hidden_size = 5
decoder_hidden_size = encoder_hidden_size
input_sequence_length = 6
decoder_sequence_length = 7
num_decoder_symbols = 20
start_of_sequence_id = end_of_sequence_id = 1
decoder_embeddings = variable_scope.get_variable(
"decoder_embeddings", [num_decoder_symbols, decoder_embedding_size],
initializer=init_ops.random_normal_initializer(stddev=0.1))
inputs = constant_op.constant(
0.5,
shape=[input_sequence_length, batch_size, encoder_embedding_size])
decoder_inputs = constant_op.constant(
0.4,
shape=[decoder_sequence_length, batch_size, decoder_embedding_size])
decoder_length = constant_op.constant(
decoder_sequence_length, dtype=dtypes.int32, shape=[batch_size,])
with variable_scope.variable_scope("rnn") as scope:
# setting up weights for computing the final output
output_fn = lambda x: layers.linear(x, num_decoder_symbols,
scope=scope)
# Define model
encoder_outputs, encoder_state = rnn.dynamic_rnn(
cell=core_rnn_cell_impl.GRUCell(encoder_hidden_size),
inputs=inputs,
dtype=dtypes.float32,
time_major=True,
scope=scope)
with variable_scope.variable_scope("decoder") as scope:
# Train decoder
decoder_cell = core_rnn_cell_impl.GRUCell(decoder_hidden_size)
decoder_fn_train = Seq2SeqTest._decoder_fn_with_context_state(
decoder_fn_lib.simple_decoder_fn_train(
encoder_state=encoder_state))
(decoder_outputs_train, decoder_state_train,
decoder_context_state_train) = (seq2seq.dynamic_rnn_decoder(
cell=decoder_cell,
decoder_fn=decoder_fn_train,
inputs=decoder_inputs,
sequence_length=decoder_length,
time_major=True,
scope=scope))
decoder_outputs_train = output_fn(decoder_outputs_train)
# Setup variable reuse
scope.reuse_variables()
# Inference decoder
decoder_fn_inference = Seq2SeqTest._decoder_fn_with_context_state(
decoder_fn_lib.simple_decoder_fn_inference(
output_fn=output_fn,
encoder_state=encoder_state,
embeddings=decoder_embeddings,
start_of_sequence_id=start_of_sequence_id,
end_of_sequence_id=end_of_sequence_id,
#TODO: find out why it goes to +1
maximum_length=decoder_sequence_length - 1,
num_decoder_symbols=num_decoder_symbols,
dtype=dtypes.int32))
(decoder_outputs_inference, decoder_state_inference,
decoder_context_state_inference) = (seq2seq.dynamic_rnn_decoder(
cell=decoder_cell,
decoder_fn=decoder_fn_inference,
time_major=True,
scope=scope))
# Run model
variables.global_variables_initializer().run()
(decoder_outputs_train_res, decoder_state_train_res,
decoder_context_state_train_res) = sess.run([
decoder_outputs_train, decoder_state_train,
decoder_context_state_train
])
(decoder_outputs_inference_res, decoder_state_inference_res,
decoder_context_state_inference_res) = sess.run([
decoder_outputs_inference, decoder_state_inference,
decoder_context_state_inference
])
# Assert outputs
self.assertEqual((decoder_sequence_length, batch_size,
num_decoder_symbols), decoder_outputs_train_res.shape)
self.assertEqual((batch_size, num_decoder_symbols),
decoder_outputs_inference_res.shape[1:3])
self.assertEqual(decoder_sequence_length,
decoder_context_state_inference_res)
self.assertEqual((batch_size, decoder_hidden_size),
decoder_state_train_res.shape)
self.assertEqual((batch_size, decoder_hidden_size),
decoder_state_inference_res.shape)
self.assertEqual(decoder_sequence_length,
decoder_context_state_train_res)
# The dynamic decoder might end earlier than `maximal_length`
# under inference
self.assertGreaterEqual(decoder_sequence_length,
decoder_state_inference_res.shape[0])
def generative_net(z, data_size):
return layers.linear(z, data_size)
def generative_net(z, data_size): return layers.linear(z, data_size)
def build():
"""Builds the Tensorflow graph."""
inputs, labels, lengths = None, None, None
if mode in ('train', 'eval'):
if isinstance(no_event_label, numbers.Number):
label_shape = []
else:
label_shape = [len(no_event_label)]
inputs, labels, lengths = magenta.common.get_padded_batch(
sequence_example_file_paths,
hparams.batch_size,
input_size,
label_shape=label_shape,
shuffle=mode == 'train')
elif mode == 'generate':
inputs = tf.placeholder(tf.float32,
[hparams.batch_size, None, input_size])
if isinstance(encoder_decoder,
magenta.music.OneHotIndexEventSequenceEncoderDecoder):
expanded_inputs = tf.one_hot(
tf.cast(tf.squeeze(inputs, axis=-1), tf.int64),
encoder_decoder.input_depth)
else:
expanded_inputs = inputs
dropout_keep_prob = 1.0 if mode == 'generate' else hparams.dropout_keep_prob
if hparams.use_cudnn:
outputs, initial_state, final_state = make_cudnn(
expanded_inputs,
hparams.rnn_layer_sizes,
hparams.batch_size,
mode,
dropout_keep_prob=dropout_keep_prob,
residual_connections=hparams.residual_connections)
else:
cell = make_rnn_cell(
hparams.rnn_layer_sizes,
dropout_keep_prob=dropout_keep_prob,
attn_length=hparams.attn_length,
residual_connections=hparams.residual_connections)
initial_state = cell.zero_state(hparams.batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(
cell,
inputs,
sequence_length=lengths,
initial_state=initial_state,
swap_memory=True)
outputs_flat = magenta.common.flatten_maybe_padded_sequences(
outputs, lengths)
if isinstance(num_classes, numbers.Number):
num_logits = num_classes
else:
num_logits = sum(num_classes)
logits_flat = contrib_layers.linear(outputs_flat, num_logits)
if mode in ('train', 'eval'):
labels_flat = magenta.common.flatten_maybe_padded_sequences(
labels, lengths)
if isinstance(num_classes, numbers.Number):
softmax_cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels_flat, logits=logits_flat)
predictions_flat = tf.argmax(logits_flat, axis=1)
else:
logits_offsets = np.cumsum([0] + num_classes)
softmax_cross_entropy = []
predictions = []
for i in range(len(num_classes)):
softmax_cross_entropy.append(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels_flat[:, i],
logits=logits_flat[:, logits_offsets[i]:
logits_offsets[i + 1]]))
predictions.append(
tf.argmax(
logits_flat[:,
logits_offsets[i]:logits_offsets[i +
1]],
axis=1))
predictions_flat = tf.stack(predictions, 1)
correct_predictions = tf.to_float(
tf.equal(labels_flat, predictions_flat))
event_positions = tf.to_float(
tf.not_equal(labels_flat, no_event_label))
no_event_positions = tf.to_float(
tf.equal(labels_flat, no_event_label))
# Compute the total number of time steps across all sequences in the
# batch. For some models this will be different from the number of RNN
# steps.
def batch_labels_to_num_steps(batch_labels, lengths):
num_steps = 0
for labels, length in zip(batch_labels, lengths):
num_steps += encoder_decoder.labels_to_num_steps(
labels[:length])
return np.float32(num_steps)
num_steps = tf.py_func(batch_labels_to_num_steps,
[labels, lengths], tf.float32)
if mode == 'train':
loss = tf.reduce_mean(softmax_cross_entropy)
perplexity = tf.exp(loss)
accuracy = tf.reduce_mean(correct_predictions)
event_accuracy = (
tf.reduce_sum(correct_predictions * event_positions) /
tf.reduce_sum(event_positions))
no_event_accuracy = (
tf.reduce_sum(correct_predictions * no_event_positions) /
tf.reduce_sum(no_event_positions))
loss_per_step = tf.reduce_sum(
softmax_cross_entropy) / num_steps
perplexity_per_step = tf.exp(loss_per_step)
optimizer = tf.train.AdamOptimizer(
learning_rate=hparams.learning_rate)
train_op = contrib_slim.learning.create_train_op(
loss, optimizer, clip_gradient_norm=hparams.clip_norm)
tf.add_to_collection('train_op', train_op)
vars_to_summarize = {
'loss': loss,
'metrics/perplexity': perplexity,
'metrics/accuracy': accuracy,
'metrics/event_accuracy': event_accuracy,
'metrics/no_event_accuracy': no_event_accuracy,
'metrics/loss_per_step': loss_per_step,
'metrics/perplexity_per_step': perplexity_per_step,
}
elif mode == 'eval':
vars_to_summarize, update_ops = contrib_metrics.aggregate_metric_map(
{
'loss':
tf.metrics.mean(softmax_cross_entropy),
'metrics/accuracy':
tf.metrics.accuracy(labels_flat, predictions_flat),
'metrics/per_class_accuracy':
tf.metrics.mean_per_class_accuracy(
labels_flat, predictions_flat, num_classes),
'metrics/event_accuracy':
tf.metrics.recall(event_positions,
correct_predictions),
'metrics/no_event_accuracy':
tf.metrics.recall(no_event_positions,
correct_predictions),
'metrics/loss_per_step':
tf.metrics.mean(tf.reduce_sum(softmax_cross_entropy) /
num_steps,
weights=num_steps),
})
for updates_op in update_ops.values():
tf.add_to_collection('eval_ops', updates_op)
# Perplexity is just exp(loss) and doesn't need its own update op.
vars_to_summarize['metrics/perplexity'] = tf.exp(
vars_to_summarize['loss'])
vars_to_summarize['metrics/perplexity_per_step'] = tf.exp(
vars_to_summarize['metrics/loss_per_step'])
for var_name, var_value in six.iteritems(vars_to_summarize):
tf.summary.scalar(var_name, var_value)
tf.add_to_collection(var_name, var_value)
elif mode == 'generate':
temperature = tf.placeholder(tf.float32, [])
if isinstance(num_classes, numbers.Number):
softmax_flat = tf.nn.softmax(
tf.div(logits_flat, tf.fill([num_classes], temperature)))
softmax = tf.reshape(softmax_flat,
[hparams.batch_size, -1, num_classes])
else:
logits_offsets = np.cumsum([0] + num_classes)
softmax = []
for i in range(len(num_classes)):
sm = tf.nn.softmax(
tf.div(
logits_flat[:,
logits_offsets[i]:logits_offsets[i +
1]],
tf.fill([num_classes[i]], temperature)))
sm = tf.reshape(sm,
[hparams.batch_size, -1, num_classes[i]])
softmax.append(sm)
tf.add_to_collection('inputs', inputs)
tf.add_to_collection('temperature', temperature)
tf.add_to_collection('softmax', softmax)
# Flatten state tuples for metagraph compatibility.
for state in tf_nest.flatten(initial_state):
tf.add_to_collection('initial_state', state)
for state in tf_nest.flatten(final_state):
tf.add_to_collection('final_state', state)
def vgg_16(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
dataset='cifar',
scope='vgg_16'):
"""Oxford Net VGG 16-Layers version D Example.
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
outputs. Useful to remove unnecessary dimensions for classification.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
def ConvBatchRelu(layer_input, n_output_plane, name):
with variable_scope.variable_scope(name):
output = layers.conv2d(layer_input,
n_output_plane, [3, 3],
scope='conv')
output = layers.batch_norm(output,
center=True,
scale=True,
activation_fn=tf.nn.relu,
is_training=is_training)
return output
filters = [
64, 64, 128, 128, 256, 256, 256, 512, 512, 512, 512, 512, 512,
512
]
if dataset == 'f_mnist':
filters = [_ // 4 for _ in filters]
elif dataset != 'cifar':
raise NotImplementedError(
"Dataset {} is not supported!".format(dataset))
net = ConvBatchRelu(inputs, filters[0], 'conv1_1')
net = ConvBatchRelu(net, filters[1], 'conv1_2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = ConvBatchRelu(net, filters[2], 'conv2_1')
net = ConvBatchRelu(net, filters[3], 'conv2_2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = ConvBatchRelu(net, filters[4], 'conv3_1')
net = ConvBatchRelu(net, filters[5], 'conv3_2')
net = ConvBatchRelu(net, filters[6], 'conv3_3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = ConvBatchRelu(net, filters[7], 'conv4_1')
net = ConvBatchRelu(net, filters[8], 'conv4_2')
net = ConvBatchRelu(net, filters[9], 'conv4_3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = ConvBatchRelu(net, filters[10], 'conv5_1')
net = ConvBatchRelu(net, filters[11], 'conv5_2')
net = ConvBatchRelu(net, filters[12], 'conv5_3')
if dataset == 'cifar':
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.flatten(net, scope='flatten6')
net = layers_lib.dropout(net,
0.5,
is_training=is_training,
scope='dropout6')
net = layers.relu(net, filters[13])
net = layers_lib.dropout(net,
0.5,
is_training=is_training,
scope='dropout6')
net = layers.linear(net, num_classes)
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(
end_points_collection)
end_points[sc.name + '/fc8'] = net
return net, end_points
def make_cudnn(inputs,
rnn_layer_sizes,
batch_size,
mode,
dropout_keep_prob=1.0,
residual_connections=False):
"""Builds a sequence of cuDNN LSTM layers from the given hyperparameters.
Args:
inputs: A tensor of RNN inputs.
rnn_layer_sizes: A list of integer sizes (in units) for each layer of the
RNN.
batch_size: The number of examples per batch.
mode: 'train', 'eval', or 'generate'. For 'generate',
CudnnCompatibleLSTMCell will be used.
dropout_keep_prob: The float probability to keep the output of any given
sub-cell.
residual_connections: Whether or not to use residual connections.
Returns:
outputs: A tensor of RNN outputs, with shape
`[batch_size, inputs.shape[1], rnn_layer_sizes[-1]]`.
initial_state: The initial RNN states, a tuple with length
`len(rnn_layer_sizes)` of LSTMStateTuples.
final_state: The final RNN states, a tuple with length
`len(rnn_layer_sizes)` of LSTMStateTuples.
"""
cudnn_inputs = tf.transpose(inputs, [1, 0, 2])
if len(set(rnn_layer_sizes)) == 1 and not residual_connections:
initial_state = tuple(
contrib_rnn.LSTMStateTuple(
h=tf.zeros([batch_size, num_units], dtype=tf.float32),
c=tf.zeros([batch_size, num_units], dtype=tf.float32))
for num_units in rnn_layer_sizes)
if mode != 'generate':
# We can make a single call to CudnnLSTM since all layers are the same
# size and we aren't using residual connections.
cudnn_initial_state = state_tuples_to_cudnn_lstm_state(
initial_state)
cell = contrib_cudnn_rnn.CudnnLSTM(num_layers=len(rnn_layer_sizes),
num_units=rnn_layer_sizes[0],
direction='unidirectional',
dropout=1.0 - dropout_keep_prob)
cudnn_outputs, cudnn_final_state = cell(
cudnn_inputs,
initial_state=cudnn_initial_state,
training=mode == 'train')
final_state = cudnn_lstm_state_to_state_tuples(cudnn_final_state)
else:
# At generation time we use CudnnCompatibleLSTMCell.
cell = contrib_rnn.MultiRNNCell([
contrib_cudnn_rnn.CudnnCompatibleLSTMCell(num_units)
for num_units in rnn_layer_sizes
])
cudnn_outputs, final_state = tf.nn.dynamic_rnn(
cell,
cudnn_inputs,
initial_state=initial_state,
time_major=True,
scope='cudnn_lstm/rnn')
else:
# We need to make multiple calls to CudnnLSTM, keeping the initial and final
# states at each layer.
initial_state = []
final_state = []
for i in range(len(rnn_layer_sizes)):
# If we're using residual connections and this layer is not the same size
# as the previous layer, we need to project into the new size so the
# (projected) input can be added to the output.
if residual_connections:
if i == 0 or rnn_layer_sizes[i] != rnn_layer_sizes[i - 1]:
cudnn_inputs = contrib_layers.linear(
cudnn_inputs, rnn_layer_sizes[i])
layer_initial_state = (contrib_rnn.LSTMStateTuple(
h=tf.zeros([batch_size, rnn_layer_sizes[i]], dtype=tf.float32),
c=tf.zeros([batch_size, rnn_layer_sizes[i]],
dtype=tf.float32)), )
if mode != 'generate':
cudnn_initial_state = state_tuples_to_cudnn_lstm_state(
layer_initial_state)
cell = contrib_cudnn_rnn.CudnnLSTM(
num_layers=1,
num_units=rnn_layer_sizes[i],
direction='unidirectional',
dropout=1.0 - dropout_keep_prob)
cudnn_outputs, cudnn_final_state = cell(
cudnn_inputs,
initial_state=cudnn_initial_state,
training=mode == 'train')
layer_final_state = cudnn_lstm_state_to_state_tuples(
cudnn_final_state)
else:
# At generation time we use CudnnCompatibleLSTMCell.
cell = contrib_rnn.MultiRNNCell([
contrib_cudnn_rnn.CudnnCompatibleLSTMCell(
rnn_layer_sizes[i])
])
cudnn_outputs, layer_final_state = tf.nn.dynamic_rnn(
cell,
cudnn_inputs,
initial_state=layer_initial_state,
time_major=True,
scope='cudnn_lstm/rnn' if i == 0 else 'cudnn_lstm_%d/rnn' %
i)
if residual_connections:
cudnn_outputs += cudnn_inputs
cudnn_inputs = cudnn_outputs
initial_state += layer_initial_state
final_state += layer_final_state
outputs = tf.transpose(cudnn_outputs, [1, 0, 2])
return outputs, tuple(initial_state), tuple(final_state)
def train():
samples = tf.placeholder(tf.float32,
[None, None, INPUT_SIZE]) # (batch, time, in)
ground_truth = tf.placeholder(tf.float32,
[None, OUTPUT_SIZE]) # (batch, out)
cell, initial_state = create_model(model=FLAGS.model,
num_cells=[FLAGS.rnn_cells] *
FLAGS.rnn_layers,
batch_size=FLAGS.batch_size)
rnn_outputs, rnn_states = tf.nn.dynamic_rnn(cell,
samples,
dtype=tf.float32,
initial_state=initial_state)
# Split the outputs of the RNN into the actual outputs and the state update gate
rnn_outputs, updated_states = split_rnn_outputs(FLAGS.model, rnn_outputs)
out = layers.linear(inputs=rnn_outputs[:, -1, :], num_outputs=OUTPUT_SIZE)
# Compute L2 loss
mse = tf.nn.l2_loss(ground_truth - out) / FLAGS.batch_size
# Compute loss for each updated state
budget_loss = compute_budget_loss(FLAGS.model, mse, updated_states,
FLAGS.cost_per_sample)
# Combine all losses
loss = mse + budget_loss
# Optimizer
opt, grads_and_vars = compute_gradients(loss, FLAGS.learning_rate,
FLAGS.grad_clip)
train_fn = opt.apply_gradients(grads_and_vars)
sess = tf.Session()
log_dir = os.path.join(FLAGS.logdir,
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
valid_writer = tf.summary.FileWriter(log_dir + '/val')
sess.run(tf.global_variables_initializer())
try:
num_iters = 0
while True:
# Generate new batch and perform SGD update
x, y = generate_batch(min_val=MIN_VAL,
max_val=MAX_VAL,
seq_length=FLAGS.sequence_length,
batch_size=FLAGS.batch_size)
sess.run([train_fn], feed_dict={samples: x, ground_truth: y})
num_iters += 1
# Evaluate on validation data generated on the fly
if num_iters % FLAGS.evaluate_every == 0:
valid_error, valid_steps = 0., 0.
for _ in range(FLAGS.validation_batches):
valid_x, valid_y = generate_batch(
min_val=MIN_VAL,
max_val=MAX_VAL,
seq_length=FLAGS.sequence_length,
batch_size=FLAGS.batch_size)
valid_iter_error, valid_used_inputs = sess.run(
[mse, updated_states],
feed_dict={
samples: valid_x,
ground_truth: valid_y
})
valid_error += valid_iter_error
if valid_used_inputs is not None:
valid_steps += compute_used_samples(valid_used_inputs)
else:
valid_steps += FLAGS.sequence_length
valid_error /= FLAGS.validation_batches
valid_steps /= FLAGS.validation_batches
valid_writer.add_summary(scalar_summary('error', valid_error),
num_iters)
valid_writer.add_summary(
scalar_summary('used_samples',
valid_steps / FLAGS.sequence_length),
num_iters)
valid_writer.flush()
print("Iteration %d, "
"validation error: %.7f, "
"validation samples: %.2f%%" %
(num_iters, valid_error,
100. * valid_steps / FLAGS.sequence_length))
except KeyboardInterrupt:
pass
def main(args):
"""Main function to train the model.
Args:
args: Parsed arguments.
Returns:
Execution status defined by `constants.ExitCode`.
"""
# Validate paths.
if not validate_paths(args):
return constants.ExitCode.INVALID_PATH
# Extract paths.
input_dir = args.input_dir
model_dir = args.model_dir
log_dir = args.log_dir
existing_model = args.existing_model
# Extract model parameters.
batch_size = args.batch_size
dropout_pkeep = args.dropout_pkeep
hidden_state_size = args.hidden_state_size
hidden_layer_size = args.hidden_layer_size
learning_rate = args.learning_rate
# Extract additional flags.
debug = args.debug
validation = args.validation
# Split corpus for training and validation.
# validation_text will be empty if validation is False.
code_text, validation_text, input_ranges = utils.read_data_files(
input_dir, validation=validation)
# Bail out if we don't have enough corpus for training.
if len(code_text) < batch_size * constants.TRAINING_SEQLEN + 1:
return constants.ExitCode.CORPUS_TOO_SMALL
# Get corpus files info. Will be used in debug mode to generate sample text.
files_info_list = []
if debug:
files_info_list = utils.get_files_info(input_dir)
assert files_info_list
# Calculate validation batch size. It will be 0 if we choose not to validate.
validation_batch_size = len(validation_text) // constants.VALIDATION_SEQLEN
# Display some stats on the data.
epoch_size = len(code_text) // (batch_size * constants.TRAINING_SEQLEN)
utils.print_data_stats(len(code_text), len(validation_text), epoch_size)
# Set graph-level random seed, so any random sequence generated in this
# graph is repeatable. It could also be removed.
tf.set_random_seed(0)
# Define placeholder for learning rate, dropout and batch size.
lr = tf.placeholder(tf.float32, name='lr')
pkeep = tf.placeholder(tf.float32, name='pkeep')
batchsize = tf.placeholder(tf.int32, name='batchsize')
# Input data.
input_bytes = tf.placeholder(tf.uint8, [None, None], name='input_bytes')
input_onehot = tf.one_hot(input_bytes, constants.ALPHA_SIZE, 1.0, 0.0)
# Expected outputs = same sequence shifted by 1, since we are trying to
# predict the next character.
expected_bytes = tf.placeholder(tf.uint8, [None, None],
name='expected_bytes')
expected_onehot = tf.one_hot(expected_bytes, constants.ALPHA_SIZE, 1.0,
0.0)
# Input state.
hidden_state = tf.placeholder(
tf.float32, [None, hidden_state_size * hidden_layer_size],
name='hidden_state')
# "naive dropout" implementation.
cells = [rnn.GRUCell(hidden_state_size) for _ in range(hidden_layer_size)]
dropcells = [
rnn.DropoutWrapper(cell, input_keep_prob=pkeep) for cell in cells
]
multicell = rnn.MultiRNNCell(dropcells, state_is_tuple=False)
multicell = rnn.DropoutWrapper(multicell, output_keep_prob=pkeep)
output_raw, next_state = tf.nn.dynamic_rnn(multicell,
input_onehot,
dtype=tf.float32,
initial_state=hidden_state)
next_state = tf.identity(next_state, name='next_state')
# Reshape training outputs.
output_flat = tf.reshape(output_raw, [-1, hidden_state_size])
output_logits = layers.linear(output_flat, constants.ALPHA_SIZE)
# Reshape expected outputs.
expected_flat = tf.reshape(expected_onehot, [-1, constants.ALPHA_SIZE])
# Compute training loss.
loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=output_logits,
labels=expected_flat)
loss = tf.reshape(loss, [batchsize, -1])
# Use softmax to normalize training outputs.
output_onehot = tf.nn.softmax(output_logits, name='output_onehot')
# Use argmax to get the max value, which is the predicted bytes.
output_bytes = tf.argmax(output_onehot, 1)
output_bytes = tf.reshape(output_bytes, [batchsize, -1],
name='output_bytes')
# Choose Adam optimizer to compute gradients.
optimizer = tf.train.AdamOptimizer(lr).minimize(loss)
# Stats for display.
seqloss = tf.reduce_mean(loss, 1)
batchloss = tf.reduce_mean(seqloss)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(expected_bytes, tf.cast(output_bytes, tf.uint8)),
tf.float32))
loss_summary = tf.summary.scalar('batch_loss', batchloss)
acc_summary = tf.summary.scalar('batch_accuracy', accuracy)
summaries = tf.summary.merge([loss_summary, acc_summary])
# Init Tensorboard stuff.
# This will save Tensorboard information in folder specified in command line.
# Two sets of data are saved so that you can compare training and
# validation curves visually in Tensorboard.
timestamp = str(math.trunc(time.time()))
summary_writer = tf.summary.FileWriter(
os.path.join(log_dir, timestamp + '-training'))
validation_writer = tf.summary.FileWriter(
os.path.join(log_dir, timestamp + '-validation'))
# Init for saving models.
# They will be saved into a directory specified in command line.
saver = tf.train.Saver(max_to_keep=constants.MAX_TO_KEEP)
# For display: init the progress bar.
step_size = batch_size * constants.TRAINING_SEQLEN
frequency = constants.DISPLAY_FREQ * step_size
progress = utils.Progress(constants.DISPLAY_FREQ,
size=constants.DISPLAY_LEN,
msg='Training on next {} batches'.format(
constants.DISPLAY_FREQ))
# Set initial state.
state = np.zeros([batch_size, hidden_state_size * hidden_layer_size])
session = tf.Session()
# We continue training on exsiting model, or start with a new model.
if existing_model:
print('Continue training on existing model: {}'.format(existing_model))
try:
saver.restore(session, existing_model)
except:
print(('Failed to restore existing model since model '
'parameters do not match.'),
file=sys.stderr)
return constants.ExitCode.TENSORFLOW_ERROR
else:
print('No existing model provided. Start training with a new model.')
session.run(tf.global_variables_initializer())
# Num of bytes we have trained so far.
steps = 0
# Training loop.
for input_batch, expected_batch, epoch in utils.rnn_minibatch_sequencer(
code_text,
batch_size,
constants.TRAINING_SEQLEN,
nb_epochs=constants.EPOCHS):
# Train on one mini-batch.
feed_dict = {
input_bytes: input_batch,
expected_bytes: expected_batch,
hidden_state: state,
lr: learning_rate,
pkeep: dropout_pkeep,
batchsize: batch_size
}
_, predicted, new_state = session.run(
[optimizer, output_bytes, next_state], feed_dict=feed_dict)
# Log training data for Tensorboard display a mini-batch of sequences
# every `frequency` batches.
if debug and steps % frequency == 0:
feed_dict = {
input_bytes: input_batch,
expected_bytes: expected_batch,
hidden_state: state,
pkeep: 1.0,
batchsize: batch_size
}
predicted, seq_loss, batch_loss, acc_value, summaries_value = session.run(
[output_bytes, seqloss, batchloss, accuracy, summaries],
feed_dict=feed_dict)
utils.print_learning_learned_comparison(input_batch, predicted,
seq_loss, input_ranges,
batch_loss, acc_value,
epoch_size, steps, epoch)
summary_writer.add_summary(summaries_value, steps)
# Run a validation step every `frequency` batches.
# The validation text should be a single sequence but that's too slow.
# We cut it up and batch the pieces (slightly inaccurate).
if validation and steps % frequency == 0 and validation_batch_size:
utils.print_validation_header(len(code_text), input_ranges)
validation_x, validation_y, _ = next(
utils.rnn_minibatch_sequencer(validation_text,
validation_batch_size,
constants.VALIDATION_SEQLEN, 1))
null_state = np.zeros(
[validation_batch_size, hidden_state_size * hidden_layer_size])
feed_dict = {
input_bytes: validation_x,
expected_bytes: validation_y,
hidden_state: null_state,
pkeep: 1.0,
batchsize: validation_batch_size
}
batch_loss, acc_value, summaries_value = session.run(
[batchloss, accuracy, summaries], feed_dict=feed_dict)
utils.print_validation_stats(batch_loss, acc_value)
# Save validation data for Tensorboard.
validation_writer.add_summary(summaries_value, steps)
# Display a short text generated with the current weights and biases.
# If enabled, there will be a large output.
if debug and steps // 4 % frequency == 0:
utils.print_text_generation_header()
file_info = utils.random_element_from_list(files_info_list)
first_byte, file_size = file_info['first_byte'], file_info[
'file_size']
ry = np.array([[first_byte]])
rh = np.zeros([1, hidden_state_size * hidden_layer_size])
sample = [first_byte]
for _ in range(file_size - 1):
feed_dict = {
input_bytes: ry,
pkeep: 1.0,
hidden_state: rh,
batchsize: 1
}
ryo, rh = session.run([output_onehot, next_state],
feed_dict=feed_dict)
rc = utils.sample_from_probabilities(
ryo, topn=10 if epoch <= 1 else 2)
sample.append(rc)
ry = np.array([[rc]])
print(repr(utils.decode_to_text(sample)))
utils.print_text_generation_footer()
# Save a checkpoint every `10 * frequency` batches. Each checkpoint is
# a version of model.
if steps // 10 % frequency == 0:
saved_model_name = constants.RNN_MODEL_NAME + '_' + timestamp
saved_model_path = os.path.join(model_dir, saved_model_name)
saved_model = saver.save(session,
saved_model_path,
global_step=steps)
print('Saved model: {}'.format(saved_model))
# Display progress bar.
if debug:
progress.step(reset=steps % frequency == 0)
# Update state.
state = new_state
steps += step_size
# Save the model after training is done.
saved_model_name = constants.RNN_MODEL_NAME + '_' + timestamp
saved_model_path = os.path.join(model_dir, saved_model_name)
saved_model = saver.save(session, saved_model_path, global_step=steps)
print('Saved model: {}'.format(saved_model))
return constants.ExitCode.SUCCESS
def discriminator(x, hidden_size, scope='Discriminator', reuse=False):
with tf.variable_scope(scope, reuse=reuse):
h0 = tf.tanh(layers.linear(x, hidden_size * 2))
h1 = tf.tanh(layers.linear(h0, hidden_size * 2))
h2 = tf.tanh(layers.linear(h1, hidden_size * 2))
return tf.sigmoid(layers.linear(h2, 1))
def create_model(input_tensor, mode, hyper_params):
"""
Creates a function classifier model using a gru.
:param input_tensor: A dictionary containing all input tensors.
:param mode: If the network is training or evaluating (tf.estimator.ModeKeys)
:param hyper_params: The hyper parameters object containing {"arch": {"pkeep": Float, "sequence_length": Int,
"hidden_layer_depth": Int, "hidden_layer_size": Int, "output_dimension": Int}}
:return: The model as a dictionary of output tensors.
"""
outputs = {}
with tf.variable_scope('GruFunctionClassifier') as scope:
batch_size = hyper_params.train.batch_size
if mode == tf.estimator.ModeKeys.EVAL:
batch_size = hyper_params.train.validation_batch_size
if mode == tf.estimator.ModeKeys.PREDICT:
batch_size = 1
# Define inputs
input_tensor = tf.reshape(
input_tensor["feature"],
(batch_size, hyper_params.arch.sequence_length, 1))
Hin = tf.zeros([
batch_size, hyper_params.arch.hidden_layer_size *
hyper_params.arch.hidden_layer_depth
],
tf.float32,
name="Hin")
# Define the actual cells
cells = [
rnn.GRUCell(hyper_params.arch.hidden_layer_size)
for _ in range(hyper_params.arch.hidden_layer_depth)
]
# "naive dropout" implementation
if mode == tf.estimator.ModeKeys.TRAIN:
cells = [
rnn.DropoutWrapper(cell,
input_keep_prob=hyper_params.arch.pkeep)
for cell in cells
]
multicell = rnn.MultiRNNCell(cells, state_is_tuple=False)
if mode == tf.estimator.ModeKeys.TRAIN:
multicell = rnn.DropoutWrapper(
multicell, output_keep_prob=hyper_params.arch.pkeep
) # dropout for the softmax layer
Yr, H = tf.nn.dynamic_rnn(multicell,
input_tensor,
dtype=tf.float32,
initial_state=Hin)
H = tf.identity(H, name='H') # just to give it a name
# Softmax layer implementation:
# Flatten the first two dimension of the output [ BATCHSIZE, SEQLEN, self.hyper_params.arch.output_dim ] => [ BATCHSIZE x SEQLEN, self.hyper_params.arch.output_dim ]
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
# From the readout point of view, a value coming from a sequence time step or a minibatch item is the same thing.
# Select last output.
output = tf.transpose(Yr, [1, 0, 2])
last = tf.gather(output, int(output.get_shape()[0]) - 1)
outputs["logits"] = layers.linear(last,
hyper_params.arch.output_dimension)
outputs["probs"] = tf.nn.softmax(outputs["logits"], name="probs")
return outputs
def __init__(self, params):
"""
:param params: dictionary with fields:
"N_HIDDEN": number of hidden states
"N_BINS": number of bins on input/ output
"LEARNING_RATE": learning rate in optimizer
"""
self.params = params
tf.reset_default_graph()
self.session = tf.Session()
self.inputs = tf.placeholder(tf.float32,
(None, None, params['N_BINS']))
self.cell = tf.contrib.rnn.LSTMCell(params['N_HIDDEN'],
state_is_tuple=True)
self.batch_size = tf.shape(self.inputs)[1]
self.h_init = tf.Variable(tf.zeros([1, params['N_HIDDEN']]),
trainable=True)
self.h_init_til = tf.tile(self.h_init, [self.batch_size, 1])
self.c_init = tf.Variable(tf.zeros([1, params['N_HIDDEN']]),
trainable=True)
self.c_init_til = tf.tile(self.c_init, [self.batch_size, 1])
self.initial_state = LSTMStateTuple(self.c_init_til, self.h_init_til)
self.rnn_outputs, self.rnn_states = \
tf.nn.dynamic_rnn(self.cell,
self.inputs,
initial_state=self.initial_state,
time_major=True)
with tf.variable_scope("output"):
self.intermediate_projection = \
lambda x: layers.fully_connected(x, num_outputs=params['N_HIDDEN'])
self.final_projection = \
lambda x: layers.linear(x, num_outputs=params['N_BINS'])
self.intermediate_features = tf.map_fn(
self.intermediate_projection, self.rnn_outputs)
self.final_features = tf.map_fn(self.final_projection,
self.intermediate_features)
self.predicted_outputs = layers.softmax(self.final_features)
with tf.variable_scope("train"):
self.outputs = \
tf.placeholder(tf.float32, (None, None, params['N_BINS']))
self.mask = tf.placeholder(tf.float32,
(None, None, params['N_BINS']))
self.all_errors = losses.categorical_crossentropy(
self.outputs * self.mask, self.predicted_outputs)
self.error = tf.reduce_mean(self.all_errors)
self.train_fn = \
tf.train.AdamOptimizer(learning_rate=params['LEARNING_RATE']) \
.minimize(self.error)
# RNN # [ BATCHSIZE, SEQLEN, ALPHASIZE ] input_x = tf.one_hot(inputs, VOCAB_SIZE) input_y = tf.one_hot(targets, VOCAB_SIZE) # creating RNN cell rnn_cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE) # run RNN rnn_outputs, final_state = tf.nn.dynamic_rnn(rnn_cell, input_x, initial_state=init_state, dtype=tf.float32) # add dense layer on top of the RNN outputs rnn_outputs_flat = tf.reshape(rnn_outputs, [-1, HIDDEN_SIZE]) dense_layer = layers.linear(rnn_outputs_flat, VOCAB_SIZE) labels = tf.reshape(input_y, [-1, VOCAB_SIZE]) output_softmax = tf.nn.softmax(dense_layer) # Loss loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=dense_layer, labels=labels)) # Minimizer minimizer = tf.train.AdagradOptimizer(learning_rate=LEARNING_RATE).minimize(loss) # Gradient clipping ''' # Here's where the magic happens! grads_and_vars = minimizer.compute_gradients(loss) grad_clipping = tf.constant(5.0, name="grad_clipping")
def discriminator_0layer(H, opt, dropout, prefix='', num_outputs=1, is_reuse=None):
H = tf.squeeze(H)
biasInit = tf.constant_initializer(0.001, dtype=tf.float32)
logits = layers.linear(tf.nn.dropout(H, keep_prob=dropout), num_outputs=num_outputs, biases_initializer=biasInit,
scope=prefix + 'dis', reuse=is_reuse)
return logits
def train(self):
samples = tf.placeholder(tf.float32,
shape=[
self.BATCH_SIZE, self.SEQUENCE_LENGTH,
self.EMBEDDING_LENGTH
],
name='Samples') # (batch, time, in)
ground_truth = tf.placeholder(tf.int64,
shape=[self.BATCH_SIZE],
name='GroundTruth')
probs = tf.placeholder(
tf.float32,
shape=[self.BATCH_SIZE, self.SEQUENCE_LENGTH, 1],
name='Probs')
mask = tf.placeholder(tf.float32,
shape=[self.BATCH_SIZE, self.SEQUENCE_LENGTH, 1],
name='padding_mask')
cell, initial_state = create_model(model='skip_lstm',
num_cells=[self.HIDDEN_UNITS],
batch_size=self.BATCH_SIZE)
rnn_outputs, rnn_states = tf.nn.dynamic_rnn(
cell, samples, dtype=tf.float32, initial_state=initial_state)
# Split the outputs of the RNN into the actual outputs and the state update gate
rnn_outputs, updated_states = split_rnn_outputs(
'skip_lstm', rnn_outputs)
# print(f"\nUpdated states are {updated_states}.\n")
logits = layers.linear(inputs=rnn_outputs[:, -1, :],
num_outputs=self.OUTPUT_SIZE)
predictions = tf.argmax(logits, 1)
# Compute cross-entropy loss
printer_lab = tf.cond(
tf.math.reduce_any(
tf.logical_or(
tf.equal(tf.zeros_like(ground_truth), ground_truth),
tf.equal(tf.ones_like(ground_truth),
ground_truth))), lambda: tf.no_op(),
lambda: tf.print("Found a label out of range: ", [ground_truth]))
with tf.control_dependencies([printer_lab]):
cross_entropy_per_sample = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=ground_truth)
# cross_entropy_per_sample = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=ground_truth)
# max_ce = tf.math.maximum(cross_entropy_per_sample)
# median_ce = tf.math.median(cross_entropy_per_sample)
# printer_max = tf.Print(max_ce, [max_ce], "The maximum cross entropy is ")
# printer_median = tf.Print(median_ce, [median_ce], "The median cross entropy is ")
printer_Nan = tf.cond(
tf.math.reduce_any(tf.math.is_nan(cross_entropy_per_sample)),
lambda: tf.print("Found NaN in entropy loss",
output_stream=sys.stderr), lambda: tf.no_op())
with tf.control_dependencies([printer_Nan]):
cross_entropy = tf.reduce_mean(
tf.boolean_mask(cross_entropy_per_sample,
tf.is_finite(cross_entropy_per_sample)))
# tf.where(tf.math.is_nan(cross_entropy_per_sample),
# tf.ones(cross_entropy_per_sample.get_shape()),
# cross_entropy_per_sample))
# Compute accuracy
accuracy = tf.reduce_mean(
tf.cast(tf.equal(predictions, ground_truth), tf.float32))
# updated_states = tf.boolean_mask(updated_states, mask)
# Compute loss for each updated state
budget_loss = compute_budget_loss('skip_lstm', cross_entropy,
updated_states, self.COST_PER_SAMPLE,
mask)
# printer_Nan = tf.cond(tf.math.reduce_any(tf.math.is_nan(budget_loss)),
# lambda: tf.print("Found NaN in budget loss"), lambda: tf.no_op())
# with tf.control_dependencies([printer_Nan]):
# budget_loss = tf.where(tf.math.is_nan(budget_loss),
# tf.ones(budget_loss.get_shape()),
# budget_loss)
# Compute loss for the amount of surprisal
surprisal_loss = compute_surprisal_loss('skip_lstm', cross_entropy,
updated_states, probs,
self.SURPRISAL_COST, mask)
# Avoid encouraging to not skip.
# printer_Nan = tf.cond(tf.math.reduce_any(tf.math.is_nan(surprisal_loss)),
# lambda: tf.print("Found NaN in surprisal loss"), lambda: tf.no_op())
# with tf.control_dependencies([printer_Nan]):
# surprisal_loss = tf.where(tf.math.logical_or(tf.equal(surprisal_loss, tf.zeros_like(surprisal_loss)),
# tf.math.is_nan(surprisal_loss)), tf.ones_like(surprisal_loss),
# surprisal_loss)
loss = cross_entropy + budget_loss + surprisal_loss
loss = tf.reshape(loss, [])
loss = tf.where(tf.is_nan(loss), tf.ones_like(loss), loss)
# Optimizer
opt, grads_and_vars = compute_gradients(
loss, self.LEARNING_RATE,
1) # used to be 1 is for gradient clipping
train_fn = opt.apply_gradients(grads_and_vars)
sess = tf.Session()
# log_dir = os.path.join(self.LOG_DIR, datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
# val_writer = tf.summary.FileWriter(log_dir + '/validation')
# Initialize weights
sess.run(tf.global_variables_initializer())
# Results
train_loss_plt = np.zeros((self.NUM_EPOCHS))
loss_plt = np.zeros((self.NUM_EPOCHS, self.ITERATIONS_PER_EPOCH, 3))
val_acc_df = np.zeros((self.NUM_EPOCHS))
train_acc_df = np.zeros((self.NUM_EPOCHS))
test_acc_df = np.zeros((self.NUM_EPOCHS))
train_update_df = np.zeros((self.NUM_EPOCHS))
val_update_df = np.zeros((self.NUM_EPOCHS))
test_update_df = np.zeros((self.NUM_EPOCHS))
test_time_df = np.zeros((self.NUM_EPOCHS))
read_embs = np.zeros(
(self.TEST_ITERS * self.BATCH_SIZE * self.SEQUENCE_LENGTH,
self.EMBEDDING_LENGTH))
non_read_embs = np.zeros(
(self.TEST_ITERS * self.BATCH_SIZE * self.SEQUENCE_LENGTH,
self.EMBEDDING_LENGTH))
read_surps = np.ones(
(self.TEST_ITERS * self.BATCH_SIZE * self.SEQUENCE_LENGTH))
non_read_surps = np.ones(
(self.TEST_ITERS * self.BATCH_SIZE * self.SEQUENCE_LENGTH))
# FILE_NAME = f'hu{self.HIDDEN_UNITS}_bs{self.BATCH_SIZE}_lr{self.LEARNING_RATE}_b{self.COST_PER_SAMPLE}_s{self.SURPRISAL_COST}_t{self.TRIAL}'
try:
train_matrix, train_labels, train_probs, train_mask = self.input_fn(
split='train')
val_matrix, val_labels, val_probs, val_mask = self.input_fn(
split='val')
test_matrix, test_labels, test_probs, test_mask = self.input_fn(
split='test')
# train_loss_plt = np.empty((self.NUM_EPOCHS, self.ITERATIONS_PER_EPOCH)
for epoch in range(self.NUM_EPOCHS):
# Load the training dataset into the pipeline
# sess.run(train_model_spec['iterator_init_op'])
# sess.run(train_model_spec['samples'])
start_time = time.time()
train_accuracy, train_steps, train_loss = 0, 0, 0
for iteration in range(self.ITERATIONS_PER_EPOCH):
# Perform SGD update
# print(iteration, train_probs[iteration].shape)
out = sess.run(
[
train_fn, loss, accuracy, updated_states,
cross_entropy, budget_loss, surprisal_loss
],
feed_dict={
samples: train_matrix[iteration],
ground_truth: train_labels[iteration],
probs: train_probs[iteration],
mask: train_mask[iteration]
})
train_accuracy += out[2]
train_loss += out[1]
loss_plt[epoch][iteration] = out[
4:] # entropy, budget, surprisal
if out[3] is not None:
train_steps += compute_used_samples(
out[3] * train_mask[iteration])
else:
train_steps += np.count_nonzero(train_mask[iteration])
duration = time.time() - start_time
train_accuracy /= self.ITERATIONS_PER_EPOCH
train_loss /= self.ITERATIONS_PER_EPOCH
train_steps /= (np.count_nonzero(train_mask) / self.BATCH_SIZE)
train_loss_plt[epoch] = train_loss
train_acc_df[epoch] = train_accuracy
train_update_df[epoch] = train_steps
val_accuracy, val_loss, val_steps = 0, 0, 0
for iteration in range(self.VAL_ITERS):
val_iter_accuracy, val_iter_loss, val_used_inputs = sess.run(
[accuracy, loss, updated_states],
feed_dict={
samples: val_matrix[iteration],
ground_truth: val_labels[iteration],
probs: val_probs[iteration],
mask: val_mask[iteration]
})
val_accuracy += val_iter_accuracy
val_loss += val_iter_loss
if val_used_inputs is not None:
val_steps += compute_used_samples(val_used_inputs *
val_mask[iteration])
else:
val_steps += np.count_nonzero(val_mask[iteration])
val_accuracy /= self.VAL_ITERS
val_loss /= self.VAL_ITERS
val_steps /= (np.count_nonzero(val_mask) / self.BATCH_SIZE)
val_acc_df[epoch] = val_accuracy
val_update_df[epoch] = val_steps
# val_writer.add_summary(scalar_summary('accuracy', val_accuracy), epoch)
# val_writer.add_summary(scalar_summary('loss', val_loss), epoch)
# val_writer.add_summary(scalar_summary('used_samples', val_steps / self.SEQUENCE_LENGTH), epoch)
# val_writer.flush()
# print("Epoch %d/%d, "
# "duration: %.2f seconds, "
# "train accuracy: %.2f%%, "
# "train samples: %.2f (%.2f%%), "
# "val accuracy: %.2f%%, "
# "val samples: %.2f (%.2f%%)" % (epoch + 1,
# self.NUM_EPOCHS,
# duration,
# 100. * train_accuracy,
# train_steps,
# 100. * train_steps / self.SEQUENCE_LENGTH,
# 100. * val_accuracy,
# val_steps,
# 100. * val_steps / self.SEQUENCE_LENGTH))
#
# print("Absolute losses: entropy: %.3f, budget: %.3f, surprisal: %.3f." % (loss_abs[0], loss_abs[1], loss_abs[2]))
#
# print("Percentage losses: entropy: %.2f%%, budget: %.2f%%, surprisal: %.2f%%.\n" % (loss_perc[0], loss_perc[1], loss_perc[2]))
loss_abs = loss_plt[epoch].mean(axis=0)
loss_perc = np.divide(loss_abs, (loss_abs.sum())) * 100
self.logger.info("\nEpoch %d/%d, "
"duration: %.2f seconds, "
"train accuracy: %.2f%%, "
"train samples: %.2f%%, "
"val accuracy: %.2f%%, "
"val samples: %.2f%%" %
(epoch + 1, self.NUM_EPOCHS, duration,
100. * train_accuracy, 100. * train_steps,
100. * val_accuracy, 100. * val_steps))
self.logger.info(
"Absolute losses: entropy: %.3f, budget: %.3f, surprisal: %.3f."
% (loss_abs[0], loss_abs[1], loss_abs[2]))
self.logger.info(
"Percentage losses: entropy: %.2f%%, budget: %.2f%%, surprisal: %.2f%%."
% (loss_perc[0], loss_perc[1], loss_perc[2]))
print(
f"entropy: {loss_plt[epoch, :, 0].mean()}, budget: {loss_plt[epoch, :, 1].mean()}, surprisal: {loss_plt[epoch, :, 2].mean()}."
)
analysis_update = val_accuracy + 1e-4 > val_acc_df.max()
if analysis_update:
self.logger.info("Updating Analysis")
read_embs = np.zeros(
(self.TEST_ITERS * self.BATCH_SIZE *
self.SEQUENCE_LENGTH, self.EMBEDDING_LENGTH))
non_read_embs = np.zeros(
(self.TEST_ITERS * self.BATCH_SIZE *
self.SEQUENCE_LENGTH, self.EMBEDDING_LENGTH))
read_surps = np.full((self.TEST_ITERS * self.BATCH_SIZE *
self.SEQUENCE_LENGTH), -1)
non_read_surps = np.full(
(self.TEST_ITERS * self.BATCH_SIZE *
self.SEQUENCE_LENGTH), -1)
test_accuracy, test_loss, test_steps, t = 0, 0, 0, 0
for iteration in range(self.TEST_ITERS):
t0 = time.time()
test_iter_accuracy, test_iter_loss, test_used_inputs = sess.run(
[accuracy, loss, updated_states],
feed_dict={
samples: test_matrix[iteration],
ground_truth: test_labels[iteration],
probs: test_probs[iteration],
mask: test_mask[iteration]
})
t += time.time() - t0
test_accuracy += test_iter_accuracy
test_loss += test_iter_loss
if test_used_inputs is not None:
test_steps += compute_used_samples(
test_used_inputs * test_mask[iteration])
if analysis_update:
try:
re, nre, rs, nrs = stats_used_samples(
test_used_inputs, test_matrix[iteration],
test_probs[iteration],
test_mask[iteration])
if len(re) > 0:
read_embs[
self.BATCH_SIZE * iteration *
self.SEQUENCE_LENGTH:self.BATCH_SIZE *
iteration * self.SEQUENCE_LENGTH +
len(re)] = re
if len(nre) > 0:
non_read_embs[
self.BATCH_SIZE * iteration *
self.SEQUENCE_LENGTH:self.BATCH_SIZE *
iteration * self.SEQUENCE_LENGTH +
len(nre)] = nre
if len(rs) > 0:
read_surps[
self.BATCH_SIZE * iteration *
self.SEQUENCE_LENGTH:self.BATCH_SIZE *
iteration * self.SEQUENCE_LENGTH +
len(rs.flatten())] = rs.flatten(
) # take out flatten but should not be the problem
if len(nrs) > 0:
non_read_surps[
self.BATCH_SIZE * iteration *
self.SEQUENCE_LENGTH:self.BATCH_SIZE *
iteration * self.SEQUENCE_LENGTH +
len(nrs.flatten())] = nrs.flatten()
except Exception as e:
self.logger.info("Could not update analysis")
self.logger.error(e)
pass
else:
test_steps += np.count_nonzero(test_mask[iteration])
test_accuracy /= self.TEST_ITERS
test_loss /= self.TEST_ITERS
test_steps /= (np.count_nonzero(test_mask) / self.BATCH_SIZE)
test_time_df[epoch] = t
test_acc_df[epoch] = test_accuracy
test_update_df[epoch] = test_steps
self.logger.info("Test time: %.2f seconds, "
"test accuracy: %.2f%%, "
"test samples: %.2f%%.\n" %
(test_time_df[epoch], 100. * test_accuracy,
100. * test_steps))
if self.EARLY_STOPPING and epoch > 15:
if epoch == 16:
best_accuracy = val_acc_df.max()
best_idx = val_acc_df.argmax()
if best_accuracy < val_acc_df[epoch] + 1e-4:
best_accuracy = val_acc_df[epoch]
best_idx = epoch
elif best_idx + 15 < epoch:
val_update_df = val_update_df[:epoch]
val_acc_df = val_acc_df[:epoch]
train_acc_df = train_acc_df[:epoch]
train_update_df = train_update_df[:epoch]
loss_plt = loss_plt[:epoch]
test_acc_df = test_acc_df[:epoch]
test_update_df = test_update_df[:epoch]
test_time_df = test_time_df[:epoch]
self.logger.info(
"Training was interrupted with early stopping")
break
except KeyboardInterrupt:
self.logger.info("Training was interrupted manually")
pass
try:
df_dict = self.CONFIG_DICT
df_dict['val_acc'] = val_acc_df
df_dict['val_updates'] = val_update_df
df_dict['train_acc'] = train_acc_df
df_dict['train_updates'] = train_update_df
df_dict['test_acc'] = test_acc_df
df_dict['test_updates'] = test_update_df
df_dict['test_time'] = test_time_df
loss_plt_mean = loss_plt.mean(axis=1).transpose()
df_dict['entropy_loss'] = loss_plt_mean[0]
df_dict['budget_loss'] = loss_plt_mean[1]
df_dict['surprisal_loss'] = loss_plt_mean[2]
df = pd.DataFrame(df_dict)
df.drop(columns=['epochs', 'file_name'], inplace=True)
csv_loc = '../csvs'
if not os.path.exists(csv_loc):
os.makedirs(csv_loc)
df.to_csv(f"{csv_loc}/{self.FILE_NAME}.csv")
except Exception as e:
print(e)
self.logger.info("Could not create csvs")
pass
## Saving analysis statistics
try:
analysis_loc = '../analysis'
if not os.path.exists(analysis_loc):
os.makedirs(analysis_loc)
print("Read words")
read_words = get_words_from_embedding(self.EMBEDDING_DICT,
read_embs)
print("Skipped words")
non_read_words = get_words_from_embedding(self.EMBEDDING_DICT,
non_read_embs)
pickle.dump(read_words,
open(f"{analysis_loc}/{self.FILE_NAME}_read_vocab.pkl",
'wb'),
protocol=0)
pickle.dump(
non_read_words,
open(f"{analysis_loc}/{self.FILE_NAME}_non_read_vocab.pkl",
'wb'),
protocol=0)
read_surps = np.vstack(read_surps).flatten()
non_read_surps = np.vstack(non_read_surps).flatten()
np.save(
open(f"{analysis_loc}/{self.FILE_NAME}_read_surprisals.npy",
'wb'), read_surps[read_surps >= 0])
np.save(
open(
f"{analysis_loc}/{self.FILE_NAME}_non_read_surprisals.npy",
'wb'), non_read_surps[non_read_surps >= 0])
except Exception as e:
print(e)
self.logger.info(
"Something went wrong when reporting analysis results")
pass
sess.close()
tf.reset_default_graph()
def __init__(self, sequence_length: int, batch_size: int,
gru_internal_size: int, num_hidden_layers: int,
stats_log_dir: str):
"""Construct `RNNTextModel` with specified hyperparameters.
This model is a deep recurrent neural network that uses GRU cells for
long-term memory.
`sequence_length` is the length of each input sequence. Longer
sequences mean the model has longer-term memory, but networks that use
longer sequences (and thus, have more time steps) are harder/take
longer to learn.
`batch_size` is the number of sequences to put in each mini-batch. The
network's weights are onlt adjusted after each mini-batch.
`gru_internal_size` specifies the number of nodes inside each hidden
GRU cell layer.
`num_hidden_layers` specifies the number of hidden layers (number of
GRU cell lateys) to use in the deep RNN.
The model's loss and graph will be periodically logged to the
`stats_log_dir` directory.
"""
# Mark time this text model was initially created
self._timestamp = str(math.trunc(time.time()))
# Store hyperparameters
self._sequence_length = sequence_length
self._batch_size = batch_size
self._gru_internal_size = gru_internal_size
self._num_hidden_layers = num_hidden_layers
# ---------------------------------------------------------------------
# Graph Inputs
# ---------------------------------------------------------------------
X = tf.placeholder(tf.uint8, [None, None], name='X')
self._inputs = {
# Hyperparameters.
'learning_rate':
tf.placeholder(tf.float32, name='learning_rate'),
'batch_size':
tf.placeholder(tf.int32, name='batch_size'),
# Dimensions: [ batch_size, sequence_length ]
'X':
X,
# Dimensions: [ batch_size, sequence_length, ALPHABET_SIZE ]
'Xo':
tf.one_hot(X, ALPHABET_SIZE, 1.0, 0.0),
# Input cell state.
# Dimensions: [batch_size, gru_internal_size * num_hidden_layers]
'H_in':
tf.placeholder(
tf.float32,
[None, self._gru_internal_size * self._num_hidden_layers],
name='Hin')
}
# Define expected RNN outputs. This is used for training.
# This is the same sequence as the input sequence, but shifted by 1
# since we are trying to predict the next character.
Y_exp = tf.placeholder(tf.uint8, [None, None], name='Y_exp')
self._expected_outputs = {
# Dimensions: [ batch_size, sequence_length ]
'Y': Y_exp,
# Dimensions: [ batch_size, sequence_length, ALPHABET_SIZE ]
'Yo': tf.one_hot(Y_exp, ALPHABET_SIZE, 1.0, 0.0)
}
# ---------------------------------------------------------------------
# Hidden Layers
# ---------------------------------------------------------------------
# Define internal/hidden RNN layers. The RNN is composed of a certain
# number of hidden layers, where each node is `GruCell` that uses
# `gru_internal_size` as the internal state size of a single cell. A
# higher `gru_internal_size` means more complex state can be stored in
# a single cell.
self._cells = [
rnn.GRUCell(self._gru_internal_size)
for _ in range(self._num_hidden_layers)
]
self._multicell = rnn.MultiRNNCell(self._cells, state_is_tuple=False)
# Using `dynamic_rnn` means Tensorflow "performs fully dynamic
# unrolling" of the network. This is faster than compiling the full
# graph at initialisation time.
#
# Note that compiling the full grapgh at train time isn’t that big of
# an issue for training, because we only need to build the graph once.
# It could be a big issue, however, if we need to build the graph
# multiple times at test time. And remember, this training loop does
# occassionally process inputs via test time, through the occassional
# reports it outputs.
#
# Yr: [ batch_size, sequence_length, gru_internal_size ]
# H: [ batch_size, gru_internal_size * num_hidden_layers ]
# H_out is the last state in the sequence.
Yr, H_out = tf.nn.dynamic_rnn(self._multicell,
self._inputs['Xo'],
dtype=tf.float32,
initial_state=self._inputs['H_in'])
# ---------------------------------------------------------------------
# Outputs
# ---------------------------------------------------------------------
# Softmax layer implementation:
# Flatten the first two dimensions of the output. This performs the
# following transformation:
#
# [ batch_size, sequence_length, ALPHABET_SIZE ]
# => [ batch_size x sequence_length, ALPHABET_SIZE ]
Yflat = tf.reshape(Yr, [-1, self._gru_internal_size])
# After this transformation, apply softmax readout layer. This way, the
# weights and biases are shared across unrolled time steps. From the
# readout point of view, a value coming from a cell or a minibatch is
# the same thing.
Ylogits = layers.linear(
Yflat,
ALPHABET_SIZE) # [ batch_size x sequence_length, ALPHABET_SIZE ]
Yflat_ = tf.reshape( # [ batch_size x sequence_length, ALPHABET_SIZE ]
self._expected_outputs['Yo'], [-1, ALPHABET_SIZE])
Yo = tf.nn.softmax(
Ylogits,
name='Yo') # [ batch_size x sequence_length, ALPHABET_SIZE ]
Y = tf.argmax(Yo, 1) # [ batch_size x sequence_length ]
Y = tf.reshape(Y, [self._inputs['batch_size'], -1],
name='Y') # [ batch_size, sequence_length ]
# Store the output nodes in a dictionary for easy access later.
self._outputs = {
'Y': Y,
# Output cell state after running a time step of the recurrent
# network. We specify this just to give H_out a identifiable name.
'H_out': tf.identity(H_out, name='H_out')
}
# Commpute the loss (error) of the network.
self._loss = tf.nn.softmax_cross_entropy_with_logits( # [ batch_size x sequence_length ]
logits=Ylogits, labels=Yflat_)
self._loss = tf.reshape( # [ batch_size, sequence_length ]
self._loss, [self._inputs['batch_size'], -1])
# Used to adjust the weights at each training step, sich that the
# loss function is minimised.
self._train_step = tf.train.AdamOptimizer().minimize(self._loss)
# Stats not used to directly train the network, but are logged so they
# can be viewed by the human user.
self._sequence_loss = tf.reduce_mean(self._loss, 1)
self._batch_loss = tf.reduce_mean(self._sequence_loss)
self._batch_accuracy = tf.reduce_mean(
tf.cast(
tf.equal(self._expected_outputs['Y'],
tf.cast(self._outputs['Y'], tf.uint8)), tf.float32))
self._initialise_tf_session()
self._build_statistics(stats_log_dir)
def __call__(self, content_words, content_len, date, target, target_len):
embeddings, reg_loss = self._encoder(content_words, content_len, date)
for i in range(self.num_blocks):
with tf.variable_scope("num_blocks_{}".format(i), reuse=tf.AUTO_REUSE):
embeddings = multihead_attention(queries=embeddings,
keys=embeddings,
values=embeddings,
num_heads=self.num_heads,
dropout_rate=self.dropout_rate,
training=self.training,
causality=False)
# feed forward
embeddings = ff(embeddings, num_units=[self.feed_forward_in_dim, self.model_dim])
# shape(?,300,512)
outputs = tf.reduce_max(embeddings, axis=1)
output_feature = outputs
if self.training and self.dropout_rate > 0:
print("In training mode, use dropout")
outputs = tf.nn.dropout(outputs, keep_prob=1 - self.dropout_rate)
with tf.variable_scope("MlpLayer") as hidden_layer_scope:
outputs = layers.fully_connected(
outputs, num_outputs=self.model_dim, activation_fn=tf.nn.tanh,
scope=hidden_layer_scope, reuse=tf.AUTO_REUSE
)
outputs = layers.linear(
outputs, self.num_vocabulary, scope="Logit_layer", reuse=tf.AUTO_REUSE
)
loss = None
training_list = []
if target is not None:
non_zero_indices = tf.where(tf.not_equal(target, 0))
col_indices = tf.cast(tf.gather_nd(target, non_zero_indices), tf.int64)
expanded_target = to_dense(
SparseTensor(
indices=tf.concat([
tf.reshape(non_zero_indices[:, 0], [-1, 1]),
tf.reshape(col_indices, [-1, 1]),
], axis=1),
values=tf.ones([tf.shape(non_zero_indices)[0]], dtype=tf.float32),
dense_shape=[self._batch_size, self.num_vocabulary]
)
)
target_dist = expanded_target / tf.cast(tf.reshape(target_len, [-1, 1]), tf.float32)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
logits=outputs,
labels=tf.stop_gradient(target_dist)
) + reg_loss
)
# set differnt lr
print("**** learning_rate ****")
tvars = tf.trainable_variables()
for var in tvars:
if not var.op.name.startswith("bert"):
training_list.append(var)
return Namespace(
logit=outputs,
feature=output_feature,
loss=loss,
training_list=training_list
)
def output_fn(x): return layers.linear(x, num_decoder_symbols, scope=scope)
def train():
samples = tf.placeholder(tf.float32, [None, None, INPUT_SIZE]) # (batch, time, in)
ground_truth = tf.placeholder(tf.int64, [None]) # (batch, out)
cell, initial_state = create_model(model=FLAGS.model,
num_cells=[FLAGS.rnn_cells] * FLAGS.rnn_layers,
batch_size=FLAGS.batch_size)
rnn_outputs, rnn_states = tf.nn.dynamic_rnn(cell, samples, dtype=tf.float32, initial_state=initial_state)
# Split the outputs of the RNN into the actual outputs and the state update gate
rnn_outputs, updated_states = split_rnn_outputs(FLAGS.model, rnn_outputs)
out = layers.linear(inputs=rnn_outputs[:, -1, :], num_outputs=OUTPUT_SIZE)
# Compute cross-entropy loss
cross_entropy_per_sample = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=out, labels=ground_truth)
cross_entropy = tf.reduce_mean(cross_entropy_per_sample)
# Compute accuracy
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), ground_truth), tf.float32))
# Compute loss for each updated state
budget_loss = compute_budget_loss(FLAGS.model, cross_entropy, updated_states, FLAGS.cost_per_sample)
# Combine all losses
loss = cross_entropy + budget_loss
# Optimizer
opt, grads_and_vars = compute_gradients(loss, FLAGS.learning_rate, FLAGS.grad_clip)
train_fn = opt.apply_gradients(grads_and_vars)
sess = tf.Session()
log_dir = os.path.join(FLAGS.logdir, datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
valid_writer = tf.summary.FileWriter(log_dir + '/val')
sess.run(tf.global_variables_initializer())
try:
num_iters = 0
while True:
# Generate new batch and perform SGD update
x, y = generate_batch(FLAGS.batch_size,
FLAGS.sampling_period,
FLAGS.signal_duration,
START_PERIOD, END_PERIOD,
START_TARGET_PERIOD, END_TARGET_PERIOD)
sess.run([train_fn], feed_dict={samples: x, ground_truth: y})
num_iters += 1
# Evaluate on validation data generated on the fly
if num_iters % FLAGS.evaluate_every == 0:
valid_accuracy, valid_steps = 0., 0.
for _ in range(FLAGS.validation_batches):
valid_x, valid_y = generate_batch(FLAGS.batch_size,
FLAGS.sampling_period,
FLAGS.signal_duration,
START_PERIOD, END_PERIOD,
START_TARGET_PERIOD, END_TARGET_PERIOD)
valid_iter_accuracy, valid_used_inputs = sess.run(
[accuracy, updated_states],
feed_dict={
samples: valid_x,
ground_truth: valid_y})
valid_accuracy += valid_iter_accuracy
if valid_used_inputs is not None:
valid_steps += compute_used_samples(valid_used_inputs)
else:
valid_steps += SEQUENCE_LENGTH
valid_accuracy /= FLAGS.validation_batches
valid_steps /= FLAGS.validation_batches
valid_writer.add_summary(scalar_summary('accuracy', valid_accuracy), num_iters)
valid_writer.add_summary(scalar_summary('used_samples', valid_steps / SEQUENCE_LENGTH), num_iters)
valid_writer.flush()
print("Iteration %d, "
"validation accuracy: %.2f%%, "
"validation samples: %.2f (%.2f%%)" % (num_iters,
100. * valid_accuracy,
valid_steps,
100. * valid_steps / SEQUENCE_LENGTH))
except KeyboardInterrupt:
pass
def build_rnn(movies_cnt,
cell_type='gru',
user_aware=True,
user_cnt=None,
rating_aware=True,
rnn_unit=300,
user_embedding=300,
movie_emb_dim=300,
feed_previous=True,
loss_weights=None,
rating_with_user=False,
batch_size=32):
loss_weights = loss_weights or [10, 2]
movie_idx_ph = tf.placeholder(tf.int32, [None, None])
_, maxlen = tf.unstack(tf.shape(movie_idx_ph))
if_training = tf.placeholder_with_default(True, [])
cell = cells[cell_type](num_units=rnn_unit)
movie_embeddings = build_embedding(movies_cnt, movie_emb_dim,
'movie_embedding')
if user_aware and user_cnt is None:
raise ValueError
if user_aware:
user_idx_ph = tf.placeholder(tf.int32, [None])
if cell_type == 'lstm':
c_user_embedding = build_embedding(user_cnt,
user_embedding,
name='user_c_embedding')
h_user_embedding = build_embedding(user_cnt,
user_embedding,
name='user_h_embedding')
state = LSTMStateTuple(
c=tf.nn.embedding_lookup(c_user_embedding, user_idx_ph),
h=tf.nn.embedding_lookup(h_user_embedding, user_idx_ph))
elif cell_type == 'gru':
user_embedding = build_embedding(user_cnt,
user_embedding,
name='user_embedding')
state = tf.nn.embedding_lookup(user_embedding, user_idx_ph)
else:
state = cell.zero_state(batch_size=batch_size, dtype=tf.float32)
def _choose_best(vec, reuse=False):
with tf.variable_scope(name_or_scope='chooser', reuse=reuse) as scope:
w = tf.get_variable(name='weights',
shape=[movie_emb_dim, movies_cnt])
b = tf.get_variable(name='bias', shape=[movies_cnt])
return tf.matmul(vec, w) + b
# not using dynamic_rnn since I want to feed previous output
def walker(idx, input, outputs, state, fprev):
output, state = cell(input, state)
new_idx = tf.cond(
fprev[idx],
lambda: tf.cast(tf.argmax(_choose_best(output), 1), tf.int32),
lambda: movie_idx_ph[:, idx + 1])
input = tf.nn.embedding_lookup(movie_embeddings, new_idx)
return idx + 1, input, tf.concat(
(outputs, tf.expand_dims(output, axis=1)), axis=1), state, fprev
def cond(idx, input, outputs, state, fprev):
return idx < maxlen - 1
idx = tf.Variable(0)
input = tf.nn.embedding_lookup(movie_embeddings, movie_idx_ph[:, 0])
feed_prev = tf.placeholder(tf.bool, [None], name='feed_prev_ph')
loop_vars = [
idx, input,
tf.zeros((batch_size, 0, movie_emb_dim), dtype=tf.float32), state,
feed_prev
]
shape_invs = [
idx.get_shape(),
input.get_shape(),
tf.TensorShape((batch_size, None, movie_emb_dim)),
state.get_shape(),
feed_prev.get_shape()
]
print(len(loop_vars), len(shape_invs))
idx, last_output, outputs, state, fp = tf.while_loop(
cond, walker, loop_vars=loop_vars, shape_invariants=shape_invs)
logits = tf.reshape(outputs, (-1, rnn_unit))
logits = _choose_best(logits, reuse=True)
logits = tf.reshape(logits, (batch_size, -1, movies_cnt))
clf_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=movie_idx_ph[:, 1:])
def training_mask():
clf_mask = tf.greater(movie_idx_ph[:, 1:], tf.cast(0, dtype=tf.int32))
clf_mask = tf.cast(clf_mask, tf.float32)
return clf_mask
def val_mask():
clf_mask = tf.greater(movie_idx_ph[:, 1:], tf.cast(0, dtype=tf.int32))
clf_mask = tf.cast(clf_mask, tf.float32)
clf_mask = tf.multiply(clf_mask, tf.cast(feed_prev[1:], tf.float32))
return clf_mask
clf_mask = tf.cond(if_training, training_mask, val_mask)
clf_loss *= clf_mask
clf_loss = tf.reduce_sum(clf_loss) / tf.reduce_sum(clf_mask)
total_loss = loss_weights[0] * clf_loss
if rating_aware:
true_ratings = tf.placeholder('float', [None, None])
ratings = linear(outputs, 1)
ratings = tf.squeeze(ratings, axis=2)
rat_loss = tf.square(ratings - true_ratings)
mask = tf.greater(true_ratings, tf.cast(0, dtype=tf.float32))
mask = tf.cast(mask, tf.float32)
rat_loss *= mask
rat_loss = tf.reduce_sum(rat_loss) / tf.reduce_sum(mask)
total_loss += loss_weights[1] * rat_loss
bag = {
'base': [movie_idx_ph, total_loss, clf_loss],
'feed_prev': feed_prev,
'if_training': if_training,
'movie_embeddings': movie_embeddings
}
if user_aware:
bag['user'] = user_idx_ph
if rating_aware:
bag['ratings'] = [true_ratings, rat_loss]
return bag
pkeep = tf.placeholder(tf.float32, name='pkeep') batchsize = tf.placeholder(tf.int32, name='batchsize') # Inputs X = tf.placeholder(tf.uint32, [None, None], name='X') # [ BATCHSIZE, SEQUENCE_LENGTH ] Xo = tf.one_hot(X, ALPHABET_LENGTH, 1.0, 0.0) # [ BATCHSIZE, SEQUENCE_LENGTH , ALPHABET_LENGTH ] # Expected outputs Y_ = tf.placeholder(tf.uint8, [None, None], name='Y_') # [ BATCHSIZE, SEQUENCE_LENGTH ] Yo_ = tf.one_hot(Y_, ALPHABET_LENGTH, 1.0, 0.0) # [ BATCHSIZE, SEQUENCE_LENGTH , ALPHABET_LENGTH ] # Initial internal cell state Hin = tf.placeholder(tf.float32, [None, INTERNAL_SIZE * LAYERS], name='Hin') # [ BATCHSIZE , INTERNAL_SIZE * LAYERS ] # Deep stacked GRU cell deep_drop_cell = tfu.rnn.MultiDropoutGRUCell(size=INTERNAL_SIZE, pkeep=DROPOUT_KEEP_RATE, layers=LAYERS) # Output predictions and output state Yr, H = tf.nn.dynamic_rnn(deep_drop_cell, Xo, dtype=tf.float32, initial_state=Hin) H = tf.identity(H, name='H') Yflat = tf.reshape(Yr, [-1, INTERNAL_SIZE]) Ylogits = layers.linear(Yflat, ALPHABET_LENGTH) Yflat_ = tf.reshape(Yo_, [-1, ALPHABET_LENGTH]) loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Yflat_) loss = tf.reshape(loss, [batchsize, -1]) Yo = tf.nn.softmax(Ylogits, name='Yo') Y = tf.argmax(Yo, 1) Y = tf.reshape(Y, [batchsize, -1], name='Y') train_step = tf.train.AdamOptimizer(lr).minimize(loss)
def inference_net(x, latent_size): return layers.linear(x, latent_size)
batchsize = tf.placeholder(tf.int32, name='batchsize')
lr = tf.placeholder(tf.float32, name='lr')
pkeep = tf.placeholder(tf.float32, name='pkeep')
X = tf.placeholder(tf.uint8, [None, None], name='X') # Input vector
Xo = tf.one_hot(X, ALPHA_SIZE, 1.0, 0.0) # One Hots create vector size ALPHA_SIZE, all set 0 except character
Y_ = tf.placeholder(tf.uint8, [None, None], name='Y_') # Output tensor
Yo_ = tf.one_hot(Y_, ALPHA_SIZE, 1.0, 0.0) # OneHot our output also
Hin = tf.placeholder(tf.float32, [None, NUM_OF_GRUS*NUM_LAYERS], name='Hin') # Recurrent input states
cells = [rnn.GRUCell(NUM_OF_GRUS) for _ in range(NUM_LAYERS)] # Create all our GRU cells per layer
dropcells = [rnn.DropoutWrapper(cell,input_keep_prob=pkeep) for cell in cells] # DropOut inside RNN
multicell = rnn.MultiRNNCell(dropcells, state_is_tuple=False)
multicell = rnn.DropoutWrapper(multicell, output_keep_prob=pkeep) # DropOut for SoftMax layer
Yr, H = tf.nn.dynamic_rnn(multicell, Xo, dtype=tf.float32, initial_state=Hin) # Unrolling through time happens here
H = tf.identity(H, name='H') # Last state of sequence
Yflat = tf.reshape(Yr, [-1, NUM_OF_GRUS])
Ylogits = layers.linear(Yflat, ALPHA_SIZE)
Yflat_ = tf.reshape(Yo_, [-1, ALPHA_SIZE])
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Yflat_)
loss = tf.reshape(loss, [batchsize, -1])
Yo = tf.nn.softmax(Ylogits, name='Yo')
Y = tf.argmax(Yo, 1)
Y = tf.reshape(Y, [batchsize, -1], name="Y")
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
# Calculate Statistics for Analysis
seqloss = tf.reduce_mean(loss, 1)
batchloss = tf.reduce_mean(seqloss)
accuracy = tf.reduce_mean(tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
loss_summary = tf.summary.scalar("batch_loss", batchloss)
acc_summary = tf.summary.scalar("batch_accuracy", accuracy)
summaries = tf.summary.merge([loss_summary, acc_summary])
def main(_):
# load data, either shakespeare, or the Python source of Tensorflow itself
shakedir = FLAGS.text_dir
# shakedir = "../tensorflow/**/*.py"
codetext, valitext, bookranges = txt.read_data_files(shakedir,
validation=True)
# display some stats on the data
epoch_size = len(codetext) // (FLAGS.train_batch_size * FLAGS.seqlen)
txt.print_data_stats(len(codetext), len(valitext), epoch_size)
#
# the model (see FAQ in README.md)
#
lr = tf.placeholder(tf.float32, name='lr') # learning rate
pkeep = tf.placeholder(tf.float32, name='pkeep') # dropout parameter
batchsize = tf.placeholder(tf.int32, name='batchsize')
# inputs
X = tf.placeholder(tf.uint8, [None, None],
name='X') # [ BATCHSIZE, FLAGS.seqlen ]
Xo = tf.one_hot(X, ALPHASIZE, 1.0,
0.0) # [ BATCHSIZE, FLAGS.seqlen, ALPHASIZE ]
# expected outputs = same sequence shifted by 1 since we are trying to predict the next character
Y_ = tf.placeholder(tf.uint8, [None, None],
name='Y_') # [ BATCHSIZE, FLAGS.seqlen ]
Yo_ = tf.one_hot(Y_, ALPHASIZE, 1.0,
0.0) # [ BATCHSIZE, FLAGS.seqlen, ALPHASIZE ]
# input state
Hin = tf.placeholder(tf.float32, [None, INTERNALSIZE * NLAYERS],
name='Hin') # [ BATCHSIZE, INTERNALSIZE * NLAYERS]
# using a NLAYERS=3 layers of GRU cells, unrolled FLAGS.seqlen=30 times
# dynamic_rnn infers FLAGS.seqlen from the size of the inputs Xo
onecell = rnn.GRUCell(INTERNALSIZE)
dropcell = rnn.DropoutWrapper(onecell, input_keep_prob=pkeep)
multicell = rnn.MultiRNNCell([dropcell] * NLAYERS, state_is_tuple=False)
multicell = rnn.DropoutWrapper(multicell, output_keep_prob=pkeep)
Yr, H = tf.nn.dynamic_rnn(multicell,
Xo,
dtype=tf.float32,
initial_state=Hin)
# Yr: [ BATCHSIZE, FLAGS.seqlen, INTERNALSIZE ]
# H: [ BATCHSIZE, INTERNALSIZE*NLAYERS ] # this is the last state in the sequence
H = tf.identity(H, name='H') # just to give it a name
# Softmax layer implementation:
# Flatten the first two dimension of the output [ BATCHSIZE, FLAGS.seqlen, ALPHASIZE ] => [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
# From the readout point of view, a value coming from a cell or a minibatch is the same thing
Yflat = tf.reshape(
Yr, [-1, INTERNALSIZE]) # [ BATCHSIZE x FLAGS.seqlen, INTERNALSIZE ]
Ylogits = layers.linear(
Yflat, ALPHASIZE) # [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
Yflat_ = tf.reshape(
Yo_, [-1, ALPHASIZE]) # [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
loss = tf.nn.softmax_cross_entropy_with_logits(
logits=Ylogits, labels=Yflat_) # [ BATCHSIZE x FLAGS.seqlen ]
loss = tf.reshape(loss, [batchsize, -1]) # [ BATCHSIZE, FLAGS.seqlen ]
Yo = tf.nn.softmax(Ylogits,
name='Yo') # [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
Y = tf.argmax(Yo, 1) # [ BATCHSIZE x FLAGS.seqlen ]
Y = tf.reshape(Y, [batchsize, -1], name="Y") # [ BATCHSIZE, FLAGS.seqlen ]
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
# stats for display
seqloss = tf.reduce_mean(loss, 1)
batchloss = tf.reduce_mean(seqloss)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
loss_summary = tf.summary.scalar("batch_loss", batchloss)
acc_summary = tf.summary.scalar("batch_accuracy", accuracy)
summaries = tf.summary.merge([loss_summary, acc_summary])
# Init Tensorboard stuff. This will save Tensorboard information into a different
# folder at each run named 'log/<timestamp>/'. Two sets of data are saved so that
# you can compare training and validation curves visually in Tensorboard.
timestamp = str(math.trunc(time.time()))
summary_writer = tf.summary.FileWriter(
os.path.join(FLAGS.summaries_dir, timestamp + "-training"))
validation_writer = tf.summary.FileWriter(
os.path.join(FLAGS.summaries_dir, timestamp + "-validation"))
# Init for saving models. They will be saved into a directory named 'checkpoints'.
# Only the last checkpoint is kept.
if not os.path.exists(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
saver = tf.train.Saver(max_to_keep=1)
# for display: init the progress bar
DISPLAY_FREQ = 50
_50_BATCHES = DISPLAY_FREQ * FLAGS.train_batch_size * FLAGS.seqlen
progress = txt.Progress(DISPLAY_FREQ,
size=111 + 2,
msg="Training on next " + str(DISPLAY_FREQ) +
" batches")
# init
istate = np.zeros([FLAGS.train_batch_size,
INTERNALSIZE * NLAYERS]) # initial zero input state
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
step = 0
# training loop
for x, y_, epoch in txt.rnn_minibatch_sequencer(codetext,
FLAGS.train_batch_size,
FLAGS.seqlen,
nb_epochs=1000):
# train on one minibatch
feed_dict = {
X: x,
Y_: y_,
Hin: istate,
lr: FLAGS.learning_rate,
pkeep: FLAGS.dropout_pkeep,
batchsize: FLAGS.train_batch_size
}
_, y, ostate, smm = sess.run([train_step, Y, H, summaries],
feed_dict=feed_dict)
# save training data for Tensorboard
summary_writer.add_summary(smm, step)
# display a visual validation of progress (every 50 batches)
if step % _50_BATCHES == 0:
feed_dict = {
X: x,
Y_: y_,
Hin: istate,
pkeep: 1.0,
batchsize: FLAGS.train_batch_size
} # no dropout for validation
y, l, bl, acc = sess.run([Y, seqloss, batchloss, accuracy],
feed_dict=feed_dict)
txt.print_learning_learned_comparison(x, y, l, bookranges, bl, acc,
epoch_size, step, epoch)
# run a validation step every 50 batches
# The validation text should be a single sequence but that's too slow (1s per 1024 chars!),
# so we cut it up and batch the pieces (slightly inaccurate)
# tested: validating with 5K sequences instead of 1K is only slightly more accurate, but a lot slower.
if step % _50_BATCHES == 0 and len(valitext) > 0:
VALI_SEQLEN = 1 * 1024 # Sequence length for validation. State will be wrong at the start of each sequence.
bsize = len(valitext) // VALI_SEQLEN
txt.print_validation_header(len(codetext), bookranges)
vali_x, vali_y, _ = next(
txt.rnn_minibatch_sequencer(valitext, bsize, VALI_SEQLEN,
1)) # all data in 1 batch
vali_nullstate = np.zeros([bsize, INTERNALSIZE * NLAYERS])
feed_dict = {
X: vali_x,
Y_: vali_y,
Hin: vali_nullstate,
pkeep: 1.0, # no dropout for validation
batchsize: bsize
}
ls, acc, smm = sess.run([batchloss, accuracy, summaries],
feed_dict=feed_dict)
txt.print_validation_stats(ls, acc)
# save validation data for Tensorboard
validation_writer.add_summary(smm, step)
# display a short text generated with the current weights and biases (every 150 batches)
if step // 3 % _50_BATCHES == 0:
txt.print_text_generation_header()
ry = np.array([[txt.convert_from_alphabet(ord("K"))]])
rh = np.zeros([1, INTERNALSIZE * NLAYERS])
for k in range(1000):
ryo, rh = sess.run([Yo, H],
feed_dict={
X: ry,
pkeep: 1.0,
Hin: rh,
batchsize: 1
})
rc = txt.sample_from_probabilities(
ryo, topn=10 if epoch <= 1 else 2)
print(chr(txt.convert_to_alphabet(rc)), end="")
ry = np.array([[rc]])
txt.print_text_generation_footer()
# save a checkpoint (every 500 batches)
if step // 10 % _50_BATCHES == 0:
saver.save(sess,
FLAGS.checkpoint_dir + '/rnn_train_' + timestamp,
global_step=step)
# display progress bar
progress.step(reset=step % _50_BATCHES == 0)
# loop state around
istate = ostate
step += FLAGS.train_batch_size * FLAGS.seqlen
def _inference(self):
logging.info('...create inference')
#fw_state_list = tf.unstack(self.fw_state, axis=0)
#fw_state_tuple = tuple(
# [tf.contrib.rnn.LSTMStateTuple(fw_state_list[idx][0], fw_state_list[idx][1])
# for idx in range(self.num_layers)])
#bw_state_list = tf.unstack(self.bw_state, axis=0)
#bw_state_tuple = tuple(
# [tf.contrib.rnn.LSTMStateTuple(bw_state_list[idx][0], bw_state_list[idx][1])
# for idx in range(self.num_layers)])
fw_cells = list()
for i in range(0, self.num_layers):
if (self.cell_type == 'lstm'):
cell = rnn.LSTMCell(num_units=self.cell_sizes[i],
state_is_tuple=True)
elif (self.cell_type == 'gru'):
# change to GRU
cell = rnn.LSTMCell(num_units=self.cell_sizes[i],
state_is_tuple=True)
else:
cell = rnn.BasicRNNCell(num_units=self.cell_sizes[i])
cell = rnn.DropoutWrapper(cell, output_keep_prob=self.keep_prob)
fw_cells.append(cell)
self.fw_cells = rnn.MultiRNNCell(fw_cells, state_is_tuple=True)
if (self.direction == 2): # bidirectional
print('bidirectional')
bw_cells = list()
for i in range(0, self.num_layers):
if (self.cell_type == 'lstm'):
cell = rnn.LSTMCell(num_units=self.cell_sizes[i],
state_is_tuple=True)
elif (self.cell_type == 'gru'):
# change to GRU
cell = rnn.LSTMCell(num_units=self.cell_sizes[i],
state_is_tuple=True)
else:
cell = rnn.BasicRNNCell(num_units=self.cell_sizes[i])
cell = rnn.DropoutWrapper(cell,
output_keep_prob=self.keep_prob)
bw_cells.append(cell)
self.bw_cells = rnn.MultiRNNCell(bw_cells, state_is_tuple=True)
if (self.direction == 1):
rnn_outputs, states = tf.nn.dynamic_rnn(
self.fw_cells,
self.inputs,
#initial_state=fw_state_tuple,
sequence_length=self.seq_lengths,
dtype=tf.float32,
time_major=True)
else: # self.direction = 2
# bidirectional rnn
outputs, states = tf.nn.bidirectional_dynamic_rnn(
cell_fw=self.fw_cells,
cell_bw=self.bw_cells,
#initial_state_fw=fw_state_tuple,
#initial_state_bw=bw_state_tuple,
dtype=tf.float32,
sequence_length=self.seq_lengths,
inputs=self.inputs,
time_major=True)
rnn_outputs = tf.concat(outputs, axis=2)
# project output from rnn output size to OUTPUT_SIZE. Sometimes it is worth adding
# an extra layer here.
self.projection = lambda x: layers.linear(
x, num_outputs=self.label_classes, activation_fn=tf.nn.sigmoid)
self.logits = tf.map_fn(self.projection, rnn_outputs, name="logits")
self.probs = tf.nn.softmax(self.logits, name="probs")
self.states = states
tf.add_to_collection('probs', self.probs)