def __call__(self,
inputs,
seq_length,
is_training=False,
reuse=False,
scope=None):
'''
Add the CNN variables and operations to the graph
'''
with tf.variable_scope(scope or type(self).__name__, reuse=reuse):
#input layer
conv = Conv2dLayer(self.num_units, 3, 1)
#output layer
outlayer = FFLayer(self.output_dim,
TfActivation(None, lambda (x): x), 0)
time_steps = [inputs]
num_time_steps = 11
print inputs[1]
for i in range(num_time_steps):
forward = tf.pad(inputs[:, i + 1:, :],
[[0, 0][0, i + 1], [0, 0]])
backward = tf.pad(inputs[:, :-i - 1, :],
[[0, 0][i + 1, 0], [0, 0]])
time_steps += [forward, backward]
logits = tf.pack(time_steps, axis=3)
#apply the input layer
#logits = tf.expand_dims(inputs, 3)
for l in range(1, self.num_layers):
logits = conv(logits, seq_length, is_training,
'convlayer' + str(l))
logits = tf.nn.relu(logits)
#stack all the output channels for the final layer
logits = tf.reshape(logits, list(logits.get_shape()[0:2] + [-1]))
#convert the logits to nonsequence logits for the output layer
logits = seq_convertors.seq2nonseq(logits, seq_length)
logits = outlayer(logits, seq_length, is_training, 'outlayer')
#convert the logits to sequence logits to match expected output
logits = seq_convertors.nonseq2seq(logits, seq_length,
int(inputs.get_shape()[0]))
#create a saver
saver = tf.train.Saver()
control_ops = None
return seq_logits, seq_length, saver, control_ops
def __call__(self,
inputs,
input_seq_length,
targets=None,
target_seq_length=None,
is_training=False,
reuse=False,
scope=None):
'''
Add the neural net variables and operations to the graph
Args:
inputs: the inputs to the neural network, this is a
[batch_size x max_input_length x feature_dim] tensor
input_seq_length: The sequence lengths of the input utterances, this
is a [batch_size] dimansional vector
targets: the targets to the neural network, this is a
[batch_size x max_output_length x 1] tensor. The targets can be
used during training
target_seq_length: The sequence lengths of the target utterances,
this is a [batch_size] dimansional vector
is_training: whether or not the network is in training mode
reuse: wheter or not the variables in the network should be reused
scope: the name scope
Returns:
A quadruple containing:
- output logits
- the output logits sequence lengths as a vector
- a saver object
- a dictionary of control operations (may be empty)
'''
with tf.variable_scope(scope or type(self).__name__, reuse=reuse):
#create the input layer
inlayer = Conv1dlayer(self.num_units, self.kernel_size, 1)
#create the gated convolutional layers
dconv = GatedDilatedConvolution(self.kernel_size)
#create the fully connected layer
act = activation.TfActivation(None, tf.nn.relu)
fflayer = FFLayer(self.num_units, act)
#create the output layer
act = activation.TfActivation(None, lambda x: x)
outlayer = FFLayer(self.output_dim, act)
#apply the input layer
logits = 0
forward = inlayer(inputs, is_training, reuse, 'inlayer')
#apply the the blocks of dilated convolutions layers
for b in range(self.num_blocks):
for l in range(self.num_layers):
forward, highway = dconv(forward, 2**l, is_training, reuse,
'dconv%d-%d' % (b, l))
logits += highway
#go to nonsequential data
logits = seq_convertors.seq2nonseq(logits, input_seq_length)
#apply the relu
logits = tf.nn.relu(logits)
#apply the fully connected layer
logits = fflayer(logits, is_training, reuse, scope='FFlayer')
#apply the output layer
logits = outlayer(logits, is_training, reuse, scope='outlayer')
#go back to sequential data
logits = seq_convertors.nonseq2seq(logits, input_seq_length,
int(inputs.get_shape()[1]))
#create a saver
saver = tf.train.Saver()
return logits, input_seq_length, saver, None
def train_NN(self, config, train_important_information,
valid_important_information):
##########################
### DATASET
##########################
train_data_dir = config.get('directories',
'exp_dir') + '/train_features_dir'
valid_data_dir = config.get('directories',
'exp_dir') + '/valid_features_dir'
NN_dir = config.get('directories', 'exp_dir') + '/NN_train_dir'
if not os.path.isdir(NN_dir):
os.mkdir(NN_dir)
logdir = NN_dir + '/logdir'
if not os.path.isdir(logdir):
os.mkdir(logdir)
#########################
### SETTINGS
##########################
# Hyperparameters
initial_learning_rate = float(
config.get('simple_NN', 'initial_learning_rate'))
decay_steps = int(config.get('simple_NN', 'decay_steps'))
decay_rate = float(config.get('simple_NN', 'decay_rate'))
# Architecture
n_hidden = int(config.get('simple_NN', 'n_hidden'))
hid_layer_num = int(config.get('simple_NN', 'hidden_layer_num'))
n_input = train_important_information['input_dim']
training_epochs = int(config.get('simple_NN', 'training_epochs'))
batch_size = int(config.get('simple_NN', 'train_batch_size'))
valid_batch_total = valid_important_information['valid_batch_total']
n_classes = train_important_information['num_labels']
training_batch_total = train_important_information[
'training_batch_total']
max_input_length = train_important_information['train_utt_max_length']
max_target_length = train_important_information[
'train_label_max_length']
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
with tf.name_scope('input'):
#create the inputs placeholder
inputs = tf.placeholder(
tf.float32,
shape=[max_input_length, batch_size, n_input],
name='features')
#the length of all the input sequences
input_seq_length = tf.placeholder(tf.int32,
shape=[batch_size],
name='input_seq_length')
#split the 3D input tensor in a list of batch_size*input_dim tensors
split_inputs = tf.unstack(inputs,
name='split_inputs_training_op')
#convert the sequential data to non sequential data
nonseq_inputs = seq_convertors.seq2nonseq(
split_inputs, input_seq_length, name='inputs-processing')
with tf.name_scope('target'):
#reference labels
targets = tf.placeholder(
tf.int32,
shape=[max_target_length, batch_size, 1],
name='targets')
#the length of all the output sequences
target_seq_length = tf.placeholder(tf.int32,
shape=[batch_size],
name='output_seq_length')
# Model parameters
with tf.name_scope("weights"):
weights = {
'h' + str(i):
tf.Variable(tf.truncated_normal([n_hidden, n_hidden],
stddev=0.1),
name="h" + str(i) + "_value")
for i in range(2, hid_layer_num + 1)
}
weights['h1'] = tf.Variable(tf.truncated_normal(
[n_input, n_hidden], stddev=0.1),
name="h1_value")
weights['out'] = tf.Variable(tf.truncated_normal(
[n_hidden, n_classes], stddev=0.1),
name="weight_out_value")
with tf.name_scope("biases"):
biases = {
'b' + str(i): tf.Variable(tf.zeros([n_hidden]),
name="b" + str(i) + "_value")
for i in range(1, hid_layer_num + 1)
}
biases['out'] = tf.Variable(tf.zeros([n_classes]),
name="bias_out_value")
# Multilayer perceptron
with tf.name_scope("layer-1"):
layer_1 = tf.add(tf.matmul(nonseq_inputs, weights['h1']),
biases['b1'])
layer_out = tf.nn.tanh(layer_1)
for i in range(2, hid_layer_num + 1):
with tf.name_scope("layer-" + str(i)):
layer = tf.add(tf.matmul(layer_out, weights['h' + str(i)]),
biases['b' + str(i)])
layer_out = tf.nn.tanh(layer)
print "hidden layer " + str(i)
with tf.name_scope("hid_out"):
nonseq_logits = tf.add(tf.matmul(layer_out, weights['out']),
biases['out'])
with tf.name_scope("targets-processing"):
#split the 3D targets tensor in a list of batch_size*1 tensors
split_targets = tf.unstack(targets)
nonseq_targets = seq_convertors.seq2nonseq(
split_targets,
target_seq_length,
name="targets-processing")
#make a vector out of the targets
nonseq_targets = tf.reshape(nonseq_targets, [-1])
#one hot encode the targets
#pylint: disable=E1101
end_nonseq_targets = tf.one_hot(
nonseq_targets, int(nonseq_logits.get_shape()[1]))
with tf.name_scope('soft_max'):
# Loss and optimizer
loss = tf.nn.softmax_cross_entropy_with_logits(
logits=nonseq_logits, labels=end_nonseq_targets)
cost = tf.reduce_mean(loss, name='cost_op')
with tf.name_scope('train'):
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(
initial_learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=True)
#optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train = optimizer.minimize(cost,
global_step=global_step,
name='train_op')
with tf.name_scope('Accuracy'):
# Prediction
correct_prediction = tf.equal(tf.argmax(end_nonseq_targets, 1),
tf.argmax(nonseq_logits, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,
tf.float32),
name='accuracy_op')
accuracy_valid = tf.reduce_mean(tf.cast(
correct_prediction, tf.float32),
name='valid-accuracy_op')
#create a summary for our cost and accuracy
tf.summary.scalar("cost", cost)
tf.summary.scalar("train-accuracy", accuracy)
tf.summary.scalar("valid-accuracy", accuracy_valid)
tf.summary.histogram('histogram-train-accuracy', accuracy)
tf.summary.histogram('histogram-valid-accuracy', accuracy_valid)
# merge all summaries into a single "operation" which we can execute in a session
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=10000)
##########################
### TRAINING & EVALUATION
##########################
config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.95
with tf.Session(graph=g, config=config) as sess:
sess.run(tf.global_variables_initializer())
# create log writer object
writer = tf.summary.FileWriter(logdir,
graph=tf.get_default_graph())
for epoch in range(training_epochs):
avg_cost = 0.
for i in range(training_batch_total):
train_batch_x = np.load(
gzip.GzipFile(
train_data_dir + '/batch_inputs_' + str(i) +
'.npy.gz', "r"))
train_batch_y = np.load(
gzip.GzipFile(
train_data_dir + '/batch_targets_' + str(i) +
'.npy.gz', "r"))
train_input_seq_length = np.load(
gzip.GzipFile(
train_data_dir + '/batch_input_seq_length_' +
str(i) + '.npy.gz', "r"))
train_target_seq_length = np.load(
gzip.GzipFile(
train_data_dir + '/batch_output_seq_length_' +
str(i) + '.npy.gz', "r"))
# perform the operations we defined earlier on batch
_, c, summary = sess.run(
[train, cost, summary_op],
feed_dict={
inputs: train_batch_x,
targets: train_batch_y,
input_seq_length: train_input_seq_length,
target_seq_length: train_target_seq_length
})
avg_cost += c
# write log
writer.add_summary(summary,
epoch * training_batch_total + i)
train_acc = 0
for j in range(training_batch_total):
train_x = np.load(
gzip.GzipFile(
train_data_dir + '/batch_inputs_' + str(j) +
'.npy.gz', "r"))
train_y = np.load(
gzip.GzipFile(
train_data_dir + '/batch_targets_' + str(j) +
'.npy.gz', "r"))
train_x_seq_length = np.load(
gzip.GzipFile(
train_data_dir + '/batch_input_seq_length_' +
str(j) + '.npy.gz', "r"))
train_y_seq_length = np.load(
gzip.GzipFile(
train_data_dir + '/batch_output_seq_length_' +
str(j) + '.npy.gz', "r"))
train_batch_acc = sess.run(accuracy,
feed_dict={
inputs:
train_x,
targets:
train_y,
input_seq_length:
train_x_seq_length,
target_seq_length:
train_y_seq_length
})
train_acc += train_batch_acc
print "batch accuracy " + str(j)
train_acc /= (training_batch_total)
valid_acc = 0
for j in range(valid_batch_total):
validation_x = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_inputs_' + str(j) +
'.npy.gz', "r"))
validation_y = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_targets_' + str(j) +
'.npy.gz', "r"))
validation_x_seq_length = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_input_seq_length_' +
str(j) + '.npy.gz', "r"))
validation_y_seq_length = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_output_seq_length_' +
str(j) + '.npy.gz', "r"))
validation_batch_acc = sess.run(
accuracy_valid,
feed_dict={
inputs: validation_x,
targets: validation_y,
input_seq_length: validation_x_seq_length,
target_seq_length: validation_y_seq_length
})
valid_acc += validation_batch_acc
valid_acc /= valid_batch_total
#print("Epoch: %03d | AvgCost: %.3f" % (epoch + 1, avg_cost / (i + 1)), end="")
#print(" | Train/Valid ACC: %.3f/%.3f" % (train_acc, valid_acc))
accuracy_log_file = open(logdir + '/accuracy_log', "a")
accuracy_log_file.write("Epoch: %03d | AvgCost: %.3f" %
(epoch + 1, avg_cost / (i + 1)))
accuracy_log_file.write(" | Train/Valid ACC: %.3f/%.3f" %
(train_acc, valid_acc) + '\n')
accuracy_log_file.close()
saver.save(sess, NN_dir + '/model.ckpt', global_step=epoch + 1)
def decode_data(self, writer):
self.retrieved_data()
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
decode_inputs = tf.placeholder(
tf.float32,
shape=[self.max_length, self.input_dim],
name='decode_inputs')
decode_seq_length = tf.placeholder(tf.int32,
shape=[1],
name='decode_seq_length')
split_inputs = tf.unstack(tf.expand_dims(decode_inputs, 1),
name="decode_split_inputs_op")
nonseq_inputs = seq_convertors.seq2nonseq(split_inputs,
decode_seq_length)
# Multilayer perceptron
layer_1 = tf.add(tf.matmul(nonseq_inputs, self.weights_h1),
self.bias_b1)
layer_1 = tf.nn.tanh(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, self.weights_h2), self.bias_b2)
layer_2 = tf.nn.tanh(layer_2)
logits = tf.add(tf.matmul(layer_2, self.weights_out),
self.bias_out,
name="logits_op")
seq_logits = seq_convertors.nonseq2seq(logits, decode_seq_length,
len(split_inputs))
decode_logits = seq_convertors.seq2nonseq(seq_logits,
decode_seq_length)
outputs = tf.nn.softmax(decode_logits, name="final_operation")
##########################
### EVALUATION
##########################
config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.9
with tf.Session(graph=g, config=config) as sess:
#with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
for i in range(self.total_uttarences):
utt_id = self.utt_id_list[i]
utt_mat = self.utt_dict[utt_id]
input_seq_length = [utt_mat.shape[0]]
#pad the inputs
utt_mat = np.append(
utt_mat,
np.zeros(
[self.max_length - utt_mat.shape[0],
utt_mat.shape[1]]), 0)
outputs_value = sess.run('final_operation:0',
feed_dict={
'decode_inputs:0':
utt_mat,
'decode_seq_length:0':
input_seq_length
})
# print (outputs_value.shape)
# print (type(outputs_value))
#get state likelihoods by dividing by the prior
output = outputs_value / self.prior
#floor the values to avoid problems with log
np.where(output == 0, np.finfo(float).eps, output)
# print (output.shape)
# print (type(output))
#write the pseudo-likelihoods in kaldi feature format
writer.write_next_utt(utt_id, np.log(output))
#close the writer
writer.close()
def __call__(self,
inputs,
seq_length,
is_training=False,
reuse=False,
scope=None):
'''
Add the DNN variables and operations to the graph
Args:
inputs: the inputs to the neural network, this is a list containing
a [batch_size, input_dim] tensor for each time step
seq_length: The sequence lengths of the input utterances, if None
the maximal sequence length will be taken
is_training: whether or not the network is in training mode
reuse: wheter or not the variables in the network should be reused
scope: the name scope
Returns:
A triple containing:
- output logits
- the output logits sequence lengths as a vector
- a saver object
- a dictionary of control operations:
-add: add a layer to the network
-init: initialise the final layer
'''
with tf.variable_scope(scope or type(self).__name__, reuse=reuse):
#input layer
layer = FFLayer(self.num_units, self.activation)
#output layer
outlayer = FFLayer(self.output_dim,
TfActivation(None, lambda (x): x), 0)
#do the forward computation
#convert the sequential data to non sequential data
nonseq_inputs = seq_convertors.seq2nonseq(inputs, seq_length)
activations = [None] * self.num_layers
activations[0] = layer(nonseq_inputs, is_training, reuse, 'layer0')
for l in range(1, self.num_layers):
activations[l] = layer(activations[l - 1], is_training, reuse,
'layer' + str(l))
if self.layerwise_init:
#variable that determines how many layers are initialised
#in the neural net
initialisedlayers = tf.get_variable(
'initialisedlayers', [],
initializer=tf.constant_initializer(0),
trainable=False,
dtype=tf.int32)
#operation to increment the number of layers
add_layer_op = initialisedlayers.assign(initialisedlayers +
1).op
#compute the logits by selecting the activations at the layer
#that has last been added to the network, this is used for layer
#by layer initialisation
logits = tf.case([(tf.equal(initialisedlayers, tf.constant(l)),
Callable(activations[l]))
for l in range(len(activations))],
default=Callable(activations[-1]),
exclusive=True,
name='layerSelector')
logits.set_shape([None, self.num_units])
else:
logits = activations[-1]
logits = outlayer(logits, is_training, reuse,
'layer' + str(self.num_layers))
if self.layerwise_init:
#operation to initialise the final layer
init_last_layer_op = tf.initialize_variables(
tf.get_collection(tf.GraphKeys.VARIABLES,
scope=(tf.get_variable_scope().name +
'/layer' + str(self.num_layers))))
control_ops = {'add': add_layer_op, 'init': init_last_layer_op}
else:
control_ops = None
#convert the logits to sequence logits to match expected output
seq_logits = seq_convertors.nonseq2seq(logits, seq_length,
len(inputs))
#create a saver
saver = tf.train.Saver()
return seq_logits, seq_length, saver, control_ops
def train_NN(self, config, train_important_information,
valid_important_information):
##########################
### DATASET
##########################
train_data_dir = config.get('directories',
'exp_dir') + '/train_features_dir'
valid_data_dir = config.get('directories',
'exp_dir') + '/valid_features_dir'
NN_dir = config.get('directories',
'exp_dir') + '/NN_train_dir_combined_acc_loss'
if not os.path.isdir(NN_dir):
os.mkdir(NN_dir)
logdir = NN_dir + '/logdir'
if not os.path.isdir(logdir):
os.mkdir(logdir)
#########################
### SETTINGS
##########################
# Hyperparameters
changed_learning_rate = float(
config.get('simple_NN', 'initial_learning_rate'))
#decay_steps = int(config.get('simple_NN', 'decay_steps')) # we are using num_steps instead of this
decay_rate = float(config.get('simple_NN', 'decay_rate'))
# Architecture
n_hidden = int(config.get('simple_NN', 'n_hidden'))
hid_layer_num = int(config.get('simple_NN', 'hidden_layer_num'))
n_input = train_important_information['input_dim']
training_epochs = int(config.get('simple_NN', 'training_epochs'))
batch_size = int(config.get('simple_NN', 'train_batch_size'))
valid_batch_total = valid_important_information['valid_batch_total']
n_classes = train_important_information['num_labels']
training_batch_total = train_important_information[
'training_batch_total']
max_input_length = train_important_information['train_utt_max_length']
max_target_length = train_important_information[
'train_label_max_length']
num_steps = training_epochs * training_batch_total
valid_frequency = training_batch_total #means after each epoch
total_number_of_retries = 3
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
#for making the learning rate half
initial_learning_rate = tf.placeholder(tf.float32,
None,
name='initial_l_rate')
learning_rate_factor = tf.placeholder(tf.float32,
None,
name='factor_value')
with tf.name_scope('input'):
#create the inputs placeholder
inputs = tf.placeholder(
tf.float32,
shape=[max_input_length, batch_size, n_input],
name='features')
#the length of all the input sequences
input_seq_length = tf.placeholder(tf.int32,
shape=[batch_size],
name='input_seq_length')
#split the 3D input tensor in a list of batch_size*input_dim tensors
split_inputs = tf.unstack(inputs,
name='split_inputs_training_op')
#convert the sequential data to non sequential data
nonseq_inputs = seq_convertors.seq2nonseq(
split_inputs, input_seq_length, name='inputs-processing')
with tf.name_scope('target'):
#reference labels
targets = tf.placeholder(
tf.int32,
shape=[max_target_length, batch_size, 1],
name='targets')
#the length of all the output sequences
target_seq_length = tf.placeholder(tf.int32,
shape=[batch_size],
name='output_seq_length')
# Model parameters
with tf.name_scope("weights"):
weights = {
'h' + str(i):
tf.Variable(tf.truncated_normal([n_hidden, n_hidden],
stddev=0.1),
name="h" + str(i) + "_value")
for i in range(2, hid_layer_num + 1)
}
weights['h1'] = tf.Variable(tf.truncated_normal(
[n_input, n_hidden], stddev=0.1),
name="h1_value")
weights['out'] = tf.Variable(tf.truncated_normal(
[n_hidden, n_classes], stddev=0.1),
name="weight_out_value")
with tf.name_scope("biases"):
biases = {
'b' + str(i): tf.Variable(tf.zeros([n_hidden]),
name="b" + str(i) + "_value")
for i in range(1, hid_layer_num + 1)
}
biases['out'] = tf.Variable(tf.zeros([n_classes]),
name="bias_out_value")
# Multilayer perceptron
with tf.name_scope("layer-1"):
layer_1 = tf.add(tf.matmul(nonseq_inputs, weights['h1']),
biases['b1'])
layer_out = tf.nn.tanh(layer_1)
for i in range(2, hid_layer_num + 1):
with tf.name_scope("layer-" + str(i)):
layer = tf.add(tf.matmul(layer_out, weights['h' + str(i)]),
biases['b' + str(i)])
layer_out = tf.nn.tanh(layer)
with tf.name_scope("hid_out"):
nonseq_logits = tf.add(tf.matmul(layer_out, weights['out']),
biases['out'])
with tf.name_scope("targets-processing"):
#split the 3D targets tensor in a list of batch_size*1 tensors
split_targets = tf.unstack(targets)
nonseq_targets = seq_convertors.seq2nonseq(
split_targets,
target_seq_length,
name="targets-processing")
#make a vector out of the targets
nonseq_targets = tf.reshape(nonseq_targets, [-1])
#one hot encode the targets
#pylint: disable=E1101
end_nonseq_targets = tf.one_hot(
nonseq_targets, int(nonseq_logits.get_shape()[1]))
with tf.name_scope('soft_max'):
# Loss and optimizer
validation_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=nonseq_logits, labels=end_nonseq_targets)
validation_cost = tf.reduce_mean(validation_loss,
name='validation_cost_op')
train_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=nonseq_logits, labels=end_nonseq_targets)
train_cost = tf.reduce_mean(train_loss, name='train_cost_op')
with tf.name_scope('train'):
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(
initial_learning_rate,
global_step,
num_steps,
decay_rate,
staircase=True) * learning_rate_factor
#optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train = optimizer.minimize(train_cost,
global_step=global_step,
name='train_op')
with tf.name_scope('Accuracy'):
# Prediction
correct_prediction = tf.equal(tf.argmax(end_nonseq_targets, 1),
tf.argmax(nonseq_logits, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,
tf.float32),
name='accuracy_op')
accuracy_valid = tf.reduce_mean(tf.cast(
correct_prediction, tf.float32),
name='valid-accuracy_op')
#create a summary for our cost and accuracy
tf.summary.scalar("Train Loss", train_cost)
tf.summary.scalar("Validation Loss", validation_cost)
tf.summary.scalar("Training Accuracy", accuracy)
tf.summary.scalar("Validation Accuracy", accuracy_valid)
# merge all summaries into a single "operation" which we can execute in a session
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=10000)
##########################
### TRAINING & EVALUATION
##########################
config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.9
with tf.Session(graph=g, config=config) as sess:
#with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
# create log writer object
writer = tf.summary.FileWriter(logdir,
graph=tf.get_default_graph())
step = 0
epoch = 0
validation_loss = 100
validation_accuracy = 0
print "First validation loss: " + str(validation_loss)
print "First validation accuracy: " + str(
validation_accuracy) + "\n"
validation_step = step
num_retries = 0
train_batch_number = 1
train_file = 0
factor = 1.0
train_acc = 0
while step < num_steps:
train_batch_x = np.load(
gzip.GzipFile(
train_data_dir + '/batch_inputs_' + str(train_file) +
'.npy.gz', "r"))
train_batch_y = np.load(
gzip.GzipFile(
train_data_dir + '/batch_targets_' + str(train_file) +
'.npy.gz', "r"))
train_input_seq_length = np.load(
gzip.GzipFile(
train_data_dir + '/batch_input_seq_length_' +
str(train_file) + '.npy.gz', "r"))
train_target_seq_length = np.load(
gzip.GzipFile(
train_data_dir + '/batch_output_seq_length_' +
str(train_file) + '.npy.gz', "r"))
learning_rate_value, _, loss, train_batch_acc, summary = sess.run(
[learning_rate, train, train_cost, accuracy, summary_op],
feed_dict={
inputs: train_batch_x,
targets: train_batch_y,
input_seq_length: train_input_seq_length,
target_seq_length: train_target_seq_length,
learning_rate_factor: factor,
initial_learning_rate: changed_learning_rate
})
changed_learning_rate = learning_rate_value
train_acc += train_batch_acc
if factor == 0.5:
factor = 1.0
#print "Step number: "+ str(step+1) + " Training Batch Number: "+ str(train_file+1)+" Learning Rate: " + str(learning_rate_value)
train_batch_number = train_batch_number + 1
train_file = (train_batch_number % training_batch_total) - 1
if train_file == -1:
train_file = training_batch_total - 1
# write log to display in tensorboard
writer.add_summary(summary, train_batch_number)
step = step + 1
if step % valid_frequency == 0:
epoch = train_batch_number / training_batch_total
sum_batch_current_loss = 0
valid_acc = 0
for valid_file in range(valid_batch_total):
validation_x = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_inputs_' +
str(valid_file) + '.npy.gz', "r"))
validation_y = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_targets_' +
str(valid_file) + '.npy.gz', "r"))
validation_x_seq_length = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_input_seq_length_' +
str(valid_file) + '.npy.gz', "r"))
validation_y_seq_length = np.load(
gzip.GzipFile(
valid_data_dir + '/batch_output_seq_length_' +
str(valid_file) + '.npy.gz', "r"))
loss, validation_batch_acc, summary = sess.run(
[validation_cost, accuracy_valid, summary_op],
feed_dict={
inputs: validation_x,
targets: validation_y,
input_seq_length: validation_x_seq_length,
target_seq_length: validation_y_seq_length
})
sum_batch_current_loss += loss
valid_acc += validation_batch_acc
current_loss = sum_batch_current_loss / valid_batch_total
valid_acc /= valid_batch_total
#we will only check rounded 3 decimal points
current_validation_accuracy = float(
format(valid_acc, '.3f'))
train_acc /= (training_batch_total)
# writing accuracy information in a log file
accuracy_log_file = open(logdir + '/accuracy_log', "a")
print "\nEpoch: %03d Train/Valid Accuracy: %.3f/%.3f\n" % (
epoch, train_acc, valid_acc)
accuracy_log_file.write(
"Epoch: %03d | Learning Rate: %f | Train/Valid ACC: %.3f/%.3f"
% (epoch, learning_rate_value, train_acc, valid_acc) +
"\n")
accuracy_log_file.close()
train_acc = 0
if current_loss >= validation_loss or current_validation_accuracy <= validation_accuracy:
print "Make learning rate half, Current_loss: " + str(
current_loss) + " Validation_loss: " + str(
validation_loss)
print "Epoch: " + str(epoch) + " Step number: " + str(
step + 1) + " Training Batch Number: " + str(
train_file + 1) + " New Learning Rate: " + str(
learning_rate_value * .5)
factor = 0.5
step = validation_step
validation_accuracy = current_validation_accuracy
num_retries = num_retries + 1
print "Number of Retries: " + str(num_retries) + "\n"
if num_retries == total_number_of_retries:
saver.save(sess,
NN_dir + '/model.ckpt',
global_step=train_batch_number - 1)
save_batch_file = open(
NN_dir + '/save_batch_number', "w")
save_batch_file.write(str(train_batch_number - 1))
save_batch_file.close()
print "Number of retries reaches maximum, finishing training the model"
break
continue
else:
print "Keep learning rate same, Current_loss: " + str(
current_loss) + " Validation_loss: " + str(
validation_loss)
print "Epoch: " + str(epoch) + " Step number: " + str(
step + 1) + " Training Batch Number: " + str(
train_file + 1) + " New Learning Rate: " + str(
learning_rate_value) + "\n"
factor = 1.0
validation_loss = current_loss
validation_accuracy = current_validation_accuracy
validation_step = step
num_retries = 0
if step == num_steps:
saver.save(sess,
NN_dir + '/model.ckpt',
global_step=train_batch_number - 1)
save_batch_file = open(NN_dir + '/save_batch_number', "w")
save_batch_file.write(str(train_batch_number - 1))
save_batch_file.close()
def __call__(self,
inputs,
input_seq_length,
targets=None,
target_seq_length=None,
is_training=False,
reuse=False,
scope=None):
'''
Add the neural net variables and operations to the graph
Args:
inputs: the inputs to the neural network, this is a
[batch_size x max_input_length x feature_dim] tensor
input_seq_length: The sequence lengths of the input utterances, this
is a [batch_size] dimansional vector
targets: the targets to the neural network, this is a
[batch_size x max_output_length x 1] tensor. The targets can be
used during training
target_seq_length: The sequence lengths of the target utterances,
this is a [batch_size] dimansional vector
is_training: whether or not the network is in training mode
reuse: wheter or not the variables in the network should be reused
scope: the name scope
Returns:
A quadruple containing:
- output logits
- the output logits sequence lengths as a vector
- a saver object
- a dictionary of control operations (may be empty)
'''
with tf.variable_scope(scope or type(self).__name__, reuse=reuse):
#the blstm layer
blstm = BLSTMLayer(self.num_units)
#the linear output layer
outlayer = FFLayer(self.output_dim,
TfActivation(None, lambda (x): x), 0)
#do the forward computation
#add gaussian noise to the inputs
if is_training:
logits = inputs + tf.random_normal(inputs.get_shape(),
stddev=0.6)
else:
logits = inputs
for layer in range(self.num_layers):
logits = blstm(logits, input_seq_length, is_training, reuse,
'layer' + str(layer))
logits = self.activation(logits, is_training, reuse)
logits = seq_convertors.seq2nonseq(logits, input_seq_length)
logits = outlayer(logits, is_training, reuse, 'outlayer')
logits = seq_convertors.nonseq2seq(logits, input_seq_length,
int(inputs.get_shape()[1]))
#create a saver
saver = tf.train.Saver()
return logits, input_seq_length, saver, None
def decode_data(self, writer):
self.retrieved_data()
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
decode_inputs = tf.placeholder(
tf.float32,
shape=[self.max_length, self.input_dim],
name='inputs')
decode_seq_length = tf.placeholder(tf.int32,
shape=[1],
name='seq_length')
split_inputs = tf.unstack(tf.expand_dims(decode_inputs, 1),
name="decode_split_inputs_op")
nonseq_inputs = seq_convertors.seq2nonseq(split_inputs,
decode_seq_length)
inputs_img = tf.reshape(
nonseq_inputs, tf.stack([tf.shape(nonseq_inputs)[0], 7, 1,
13]))
inputs_img = tf.transpose(inputs_img, [0, 1, 3, 2])
print 'Input Img: '
print inputs_img.get_shape().as_list()
hidden_1 = self.convolution(inputs_img, self.conv1_weights,
self.conv1_biases)
pool = tf.nn.max_pool(hidden_1,
ksize=[1, 3, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID')
print 'poll_l1: '
print pool.get_shape().as_list()
hidden_2 = self.convolution(pool, self.conv2_weights,
self.conv2_biases)
shape = hidden_2.get_shape().as_list()
conv_outputs = tf.reshape(
hidden_2,
tf.stack(
[tf.shape(hidden_2)[0], shape[1] * shape[2] * shape[3]]))
print 'Outputs: '
print conv_outputs.get_shape().as_list()
# Multilayer perceptron
#-----------Start-----------
layer_1 = tf.add(tf.matmul(conv_outputs, self.weights['h1']),
self.biases['b1'])
layer_out = tf.nn.tanh(layer_1)
for i in range(2, self.hid_layer_num + 1):
layer = tf.add(
tf.matmul(layer_out, self.weights['h' + str(i)]),
self.biases['b' + str(i)])
layer_out = tf.nn.tanh(layer)
logits = tf.add(tf.matmul(layer_out, self.weights['out']),
self.biases['out'])
outputs = tf.nn.softmax(logits, name="final_operation")
#--------- End--------------------------
##########################
### EVALUATION
##########################
config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.9
with tf.Session(graph=g, config=config) as sess:
#with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
for i in range(self.total_uttarences):
utt_id = self.utt_id_list[i]
utt_mat = self.utt_dict[utt_id]
input_seq_length = [utt_mat.shape[0]]
#print "This is the input length: "+str(input_seq_length)
#pad the inputs
utt_mat = np.append(
utt_mat,
np.zeros(
[self.max_length - utt_mat.shape[0],
utt_mat.shape[1]]), 0)
outputs_value = sess.run(outputs,
feed_dict={
decode_inputs: utt_mat,
decode_seq_length:
input_seq_length
})
print str(i + 1) + " " + str(
self.total_uttarences) + " " + str(utt_id) + " " + str(
outputs_value.shape)
#get state likelihoods by dividing by the prior
output = outputs_value / self.prior
#floor the values to avoid problems with log
output = np.where(output == 0, np.finfo(float).eps, output)
# print (output.shape)
# print (type(output))
#write the pseudo-likelihoods in kaldi feature format
writer.write_next_utt(utt_id, np.log(output))
#close the writer
writer.close()
def __call__(self,
inputs,
seq_length,
is_training=False,
reuse=False,
scope=None):
'''
Add the LSTM variables and operations to the graph
Args:
inputs: the inputs to the neural network, this is a list containing
a [batch_size, input_dim] tensor for each time step
seq_length: The sequence lengths of the input utterances, if None
the maximal sequence length will be taken
is_training: whether or not the network is in training mode
reuse: wheter or not the variables in the network should be reused
scope: the name scope
Returns:
A triple containing:
- output logits
- the output logits sequence lengths as a vector
- a saver object
- a dictionary of control operations:
-add: add a layer to the network
-init: initialise the final layer
'''
with tf.variable_scope(scope or type(self).__name__, reuse=reuse):
weights = {
'out':
tf.get_variable(
'weights_out', [self.num_units, self.output_dim],
initializer=tf.contrib.layers.xavier_initializer())
}
biases = {
'out':
tf.get_variable('biases_out', [self.output_dim],
initializer=tf.constant_initializer(0))
}
#convert the sequential data to non sequential data
nonseq_inputs = seq_convertors.seq2nonseq(inputs, seq_length)
input_dim = nonseq_inputs.shape[1]
nonseq_inputs = tf.reshape(nonseq_inputs, [-1, 11, 40])
n_steps = 11
nonseq_inputs = tf.transpose(nonseq_inputs, [1, 0, 2])
keep_prob = 1
# define the lstm cell
# use the dropout in training mode
if is_training and keep_prob < 1:
lstm_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(
self.num_units,
forget_bias=0.0,
input_size=None,
activation=tf.nn.relu,
layer_norm=False,
norm_gain=1.0,
norm_shift=0.0,
dropout_keep_prob=keep_prob,
dropout_prob_seed=None)
lstm_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(
self.num_units,
forget_bias=0.0,
input_size=None,
activation=tf.nn.relu,
layer_norm=False,
norm_gain=1.0,
norm_shift=0.0,
dropout_keep_prob=1,
dropout_prob_seed=None)
# stack the lstm to form multi-layers
cell = tf.contrib.rnn.MultiRNNCell([lstm_cell] * self.num_layers,
state_is_tuple=True)
# print(int(nonseq_inputs.shape[0]))
# self._initial_state = cell.zero_state(int(nonseq_inputs.shape[0]), tf.float32)
# apply the dropout for the inputs to the first hidden layer
if is_training and keep_prob < 1:
nonseq_inputs = tf.nn.dropout(nonseq_inputs, keep_prob)
final_nonseq_inputs = tf.unstack(nonseq_inputs,
num=n_steps,
axis=0)
# Get lstm cell output initial_state=self._initial_state,
outputs, states = tf.contrib.rnn.static_rnn(cell,
final_nonseq_inputs,
dtype=tf.float32)
outputs = outputs[-1]
# Linear activation, using rnn inner loop last output
logits = tf.matmul(outputs, weights['out']) + biases['out']
# # if self.layerwise_init:
# # #variable that determines how many layers are initialised
# # #in the neural net
# # initialisedlayers = tf.get_variable(
# # 'initialisedlayers', [],
# # initializer=tf.constant_initializer(0),
# # trainable=False,
# # dtype=tf.int32)
# # #operation to increment the number of layers
# # add_layer_op = initialisedlayers.assign(initialisedlayers+1).op
# # #compute the logits by selecting the activations at the layer
# # #that has last been added to the network, this is used for layer
# # #by layer initialisation
# # logits = tf.case(
# # [(tf.equal(initialisedlayers, tf.constant(l)),
# # Callable(activations[l]))
# # for l in range(len(activations))],
# # default=Callable(activations[-1]),
# # exclusive=True, name='layerSelector')
# # logits.set_shape([None, self.num_units])
if self.layerwise_init:
#operation to initialise the final layer
init_last_layer_op = tf.initialize_variables(
tf.get_collection(tf.GraphKeys.VARIABLES,
scope=(tf.get_variable_scope().name +
'/layer' + str(self.num_layers))))
control_ops = {'add': add_layer_op, 'init': init_last_layer_op}
else:
control_ops = None
#convert the logits to sequence logits to match expected output
seq_logits = seq_convertors.nonseq2seq(logits, seq_length,
len(inputs))
#create a saver
saver = tf.train.Saver()
return seq_logits, seq_length, saver, control_ops
def __call__(self,
inputs,
seq_length,
is_training=False,
reuse=False,
scope=None):
'''
Add the DNN variables and operations to the graph
Args:
inputs: the inputs to the neural network, this is a list containing
a [batch_size, input_dim] tensor for each time step
seq_length: The sequence lengths of the input utterances, if None
the maximal sequence length will be taken
is_training: whether or not the network is in training mode
reuse: wheter or not the variables in the network should be reused
scope: the name scope
Returns:
A triple containing:
- output logits
- the output logits sequence lengths as a vector
- a saver object
- a dictionary of control operations:
-add: add a layer to the network
-init: initialise the final layer
'''
with tf.variable_scope(scope or type(self).__name__, reuse=reuse):
#input layer
layer = FFLayer(self.num_units, self.activation)
#output layer
outlayer = FFLayer(self.output_dim,
TfActivation(None, lambda x: x), 0)
#convert the sequential data to non sequential data
## if you wanna use the pure dnn, please uncommit this line
#nonseq_inputs = seq_convertors.seq2nonseq(inputs, seq_length)
activations = [None] * self.num_layers
# Define the first hidden layers
# # the conv layer
#cnn_layer = RestNet()
#cnn_layer = CnnVd6()
if self.cnn_type == 1:
print('------The Cnn Config------')
#convert the sequential data to non sequential data
nonseq_inputs = seq_convertors.seq2nonseq(inputs, seq_length)
cnn_layer = CnnLayer(self.cnn_conf)
activations[0] = cnn_layer(nonseq_inputs, is_training, reuse,
'layer0')
else:
print("Not using CNN")
# # the lstm layer, type 1
if self.lstm_type == 1:
print('------The LSTM Config------')
#convert the sequential data to non sequential data
# the inputs format is: time List(such as 777), each element is 2-D tensor like: batch_size(such as 64) x fre-dim
# the nonseq_inputs format is: batch_size x fre-dim, 2-D tensor, here the batch_size = batch_size x time
nonseq_inputs = seq_convertors.seq2nonseq(inputs, seq_length)
print(
'Type1: The lstm data process is the similar to dnn, use the stacking frame and not output state is reused'
)
lstm_layer = LSTMLayer(self.lstm_conf)
activations[0] = lstm_layer(nonseq_inputs, is_training, reuse,
'layer0')
## the lstm layer, type 2
elif self.lstm_type == 2:
print('------The LSTM Config------')
print('Type2: The lstm data process is totally sequencial')
# here we directly use the seq data, that's para: inputs
lstm_layer = LSTMLayer2(self.lstm_conf2)
# the dynamic lstm's output has the format: time x batch_size x feature_dim
seq_output = lstm_layer(inputs, seq_length, is_training, reuse,
'layer0')
# to connect the dnn, we should tran the seq output to no-seq
# so we can use directly with dnn
activations[0] = seq_convertors.seq2nonseq(
seq_output, seq_length)
## the lstm layer, type 3
elif self.lstm_type == 3:
print('------The LSTM Config------')
print('Type3: The lstm data is processed in sub-seq')
# here we directly use the seq data, that's para: inputs
lstm_layer = LSTMLayer3(self.lstm_conf3, self.max_input_length)
# the dynamic lstm's output has the format: time x batch_size x feature_dim
seq_output = lstm_layer(inputs, seq_length, is_training, reuse,
'layer0')
# to connect the dnn, we should tran the seq output to no-seq
# so we can use directly with dnn
# Note:
# the seq_output here should has the first index corresponding to the seq_length
# shape like: [seq_length, batch-size, output-dim]
activations[0] = seq_convertors.seq2nonseq(
seq_output, seq_length)
else:
print("Not using LSTM")
# define the FL hidden layers
print('------The DNN Config------')
print("use %d FL hidden layer" % (self.FL_num_layers))
for l in range(1, self.num_layers):
print("the " + str(l) + " layer's input is: " +
str(activations[l - 1].shape))
activations[l] = layer(activations[l - 1], is_training, reuse,
'layer' + str(l))
if self.layerwise_init:
#variable that determines how many layers are initialised
#in the neural net
initialisedlayers = tf.get_variable(
'initialisedlayers', [],
initializer=tf.constant_initializer(0),
trainable=False,
dtype=tf.int32)
#operation to increment the number of layers
add_layer_op = initialisedlayers.assign(initialisedlayers +
1).op
#compute the logits by selecting the activations at the layer
#that has last been added to the network, this is used for layer
#by layer initialisation
logits = tf.case([(tf.equal(initialisedlayers, tf.constant(l)),
Callable(activations[l]))
for l in range(len(activations))],
default=Callable(activations[-1]),
exclusive=True,
name='layerSelector')
logits.set_shape([None, self.num_units])
else:
logits = activations[-1]
logits = outlayer(logits, is_training, reuse,
'layer' + str(self.num_layers))
if self.layerwise_init:
#operation to initialise the final layer
init_last_layer_op = tf.initialize_variables(
tf.get_collection(
tf.GraphKeys.VARIABLES,
scope=(tf.get_variable_scope().name + '/layer' +
str(self.FL_num_layers))))
control_ops = {'add': add_layer_op, 'init': init_last_layer_op}
else:
control_ops = None
#convert the logits to sequence logits to match expected output
seq_logits = seq_convertors.nonseq2seq(logits, seq_length,
len(inputs))
#create a saver
saver = tf.train.Saver()
return seq_logits, seq_length, saver, control_ops
