diff --git a/bob/learn/tensorflow/layers/LSTM.py b/bob/learn/tensorflow/layers/LSTM.py
index de16784601e5ed58600ba830163cd8eda57c336c..0b07ad7aca3ae9963971d27eb6237e7604376756 100644
--- a/bob/learn/tensorflow/layers/LSTM.py
+++ b/bob/learn/tensorflow/layers/LSTM.py
@@ -14,10 +14,10 @@ logger = logging.getLogger("bob.learn")
def lstm(inputs, lstm_cell_size, lstm_fn=tf.contrib.rnn.BasicLSTMCell, num_time_steps=20,
output_activation_size=10, batch_size=10, scope='rnn',
weights_initializer=tf.random_normal, activation=tf.nn.relu,
- name=None, reuse=None, dropout=False, input_dropout=1.0, output_dropout=1.0):
+ name=None, reuse=None, dropout=False, input_dropout=1.0, output_dropout=1.0, full_output=False):
"""
"""
- return LSTM(lstm_cell_size=lstm_cell_size,
+ output = LSTM(lstm_cell_size=lstm_cell_size,
num_time_steps=num_time_steps,
batch_size=batch_size,
lstm_fn=lstm_fn,
@@ -30,6 +30,11 @@ def lstm(inputs, lstm_cell_size, lstm_fn=tf.contrib.rnn.BasicLSTMCell, num_time_
output_dropout=output_dropout,
name=name,
reuse=reuse)(inputs)
+ if full_output:
+ logger.info("LSTM: the number of the outputs: {0}".format(len(output)))
+ return output
+ logger.info("LSTM: the shape of the output: {0}".format(output[-1].shape))
+ return output[-1]
class LSTM(base.Layer):
@@ -95,23 +100,30 @@ class LSTM(base.Layer):
shape = inputs.get_shape().as_list()
logger.info("LSTM: the shape of the inputs: {0}".format(shape))
- input_time_steps = shape[1] # second dimension must be the number of time steps in LSTM
-
- if len(shape) == 4: # when inputs shape is 4, the last dimension must be 1
- if shape[-1] == 1: # we accept last dimension to be 1, then we just reshape it
- inputs = tf.reshape(inputs, shape=(-1, shape[1], shape[2]))
- logger.info("LSTM: after reshape, the shape of the inputs: {0}".format(inputs.get_shape().as_list()))
- else:
- raise ValueError('The shape of input must be either (batch_size, num_time_steps, input_vector_size) or '
- '(batch_size, num_time_steps, input_vector_size, 1), but it is {}'.format(shape))
-
- if input_time_steps % self.num_time_steps:
- raise ValueError('number of rows in one batch of input ({}) should be '
- 'the same as the num_time_steps of LSTM ({})'
- .format(input_time_steps, self.num_time_steps))
-
- # convert inputs into the num_time_steps list of the inputs each of shape (batch_size, input_vector_size)
- list_inputs = tf.unstack(inputs, self.num_time_steps, 1)
+ # if the input is already formatted correctly, just use it as is
+ if shape[1] == self.batch_size and shape[0] == self.num_time_steps:
+ inputs.set_shape((shape[0], None, shape[2]))
+ logger.info("LSTM: undefine batch shape inputs: {0}".format(inputs.get_shape().as_list()))
+ list_inputs = tf.unstack(inputs, self.num_time_steps, 0)
+ # here we consider all special cases
+ else:
+ input_time_steps = shape[1] # second dimension must be the number of time steps in LSTM
+
+ if len(shape) == 4: # when inputs shape is 4, the last dimension must be 1
+ if shape[-1] == 1: # we accept last dimension to be 1, then we just reshape it
+ inputs = tf.reshape(inputs, shape=(-1, shape[1], shape[2]))
+ logger.info("LSTM: after reshape, the shape of the inputs: {0}".format(inputs.get_shape().as_list()))
+ else:
+ raise ValueError('The shape of input must be either (batch_size, num_time_steps, input_vector_size) or '
+ '(batch_size, num_time_steps, input_vector_size, 1), but it is {}'.format(shape))
+
+ if input_time_steps % self.num_time_steps:
+ raise ValueError('number of rows in one batch of input ({}) should be '
+ 'the same as the num_time_steps of LSTM ({})'
+ .format(input_time_steps, self.num_time_steps))
+
+ # convert inputs into the num_time_steps list of the inputs each of shape (batch_size, input_vector_size)
+ list_inputs = tf.unstack(inputs, self.num_time_steps, 1)
# run LSTM training on the batch of inputs
# return the output (a list of self.num_time_steps outputs each of size input_vector_size)
@@ -123,6 +135,6 @@ class LSTM(base.Layer):
scope=self.scope)
# consider the output of the last cell
- return outputs[-1]
+ return outputs
# return tf.matmul(outputs[-1], self.output_activation_weights['out']) + self.output_activation_biases['out']
diff --git a/bob/learn/tensorflow/network/__init__.py b/bob/learn/tensorflow/network/__init__.py
index d83b5892af9c4aad34fb23f381c20a846ec9d366..b7fc9cc50bb7c7c5e6018ea4bee2fccf8c2b9c94 100644
--- a/bob/learn/tensorflow/network/__init__.py
+++ b/bob/learn/tensorflow/network/__init__.py
@@ -3,7 +3,7 @@ from .LightCNN9 import LightCNN9
from .Dummy import Dummy
from .MLP import MLP
from .Embedding import Embedding
-from .lstm import simple_lstm_network
+from .lstm import simple_lstm_network, double_lstm_network, triple_lstm_network
from .lstm import RegularizedLoss
from .simplemlp import mlp_network
from .simplecnn import simple2Dcnn_network
diff --git a/bob/learn/tensorflow/network/lstm.py b/bob/learn/tensorflow/network/lstm.py
index 7919767d13e7d6951506fe247c05777a57201d85..b0af20da3c91525776d84eecbb2d2409039e0a0b 100644
--- a/bob/learn/tensorflow/network/lstm.py
+++ b/bob/learn/tensorflow/network/lstm.py
@@ -69,3 +69,71 @@ def simple_lstm_network(train_data_shuffler, lstm_cell_size=64, batch_size=10,
weights_initializer=initializer, weights_regularizer=regularizer, reuse=reuse)
return graph
+
+
+def double_lstm_network(train_data_shuffler, lstm_cell_size=64, batch_size=10,
+ num_time_steps=28, num_classes=10, seed=10, reuse=False,
+ dropout=False, input_dropout=1.0, output_dropout=1.0):
+
+ if isinstance(train_data_shuffler, tf.Tensor):
+ inputs = train_data_shuffler
+ else:
+ inputs = train_data_shuffler("data", from_queue=False)
+
+ initializer = tf.contrib.layers.xavier_initializer(seed=seed)
+
+ # First LSTM layer network
+ graph = lstm(inputs, lstm_cell_size, num_time_steps=num_time_steps, batch_size=batch_size,
+ output_activation_size=num_classes, scope='lstm1', name='sync_cell_l1',
+ weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse,
+ dropout=dropout, input_dropout=input_dropout, output_dropout=output_dropout, full_output=True)
+
+ # Second LSTM layer network of twice smaller size
+ graph = lstm(graph, lstm_cell_size/2, num_time_steps=num_time_steps, batch_size=batch_size,
+ output_activation_size=num_classes, scope='lstm2', name='sync_cell_l2',
+ weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse,
+ dropout=dropout, input_dropout=input_dropout, output_dropout=output_dropout)
+
+ regularizer = None
+ # fully connect the LSTM output to the classes
+ graph = slim.fully_connected(graph, num_classes, activation_fn=None, scope='fc1',
+ weights_initializer=initializer, weights_regularizer=regularizer, reuse=reuse)
+
+ return graph
+
+
+def triple_lstm_network(train_data_shuffler, lstm_cell_size=64, batch_size=10,
+ num_time_steps=28, num_classes=10, seed=10, reuse=False,
+ dropout=False, input_dropout=1.0, output_dropout=1.0):
+
+ if isinstance(train_data_shuffler, tf.Tensor):
+ inputs = train_data_shuffler
+ else:
+ inputs = train_data_shuffler("data", from_queue=False)
+
+ initializer = tf.contrib.layers.xavier_initializer(seed=seed)
+
+ # First LSTM layer network
+ graph = lstm(inputs, lstm_cell_size, num_time_steps=num_time_steps, batch_size=batch_size,
+ output_activation_size=num_classes, scope='lstm1', name='sync_cell_l1',
+ weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse,
+ dropout=dropout, input_dropout=input_dropout, output_dropout=output_dropout, full_output=True)
+
+ # Second LSTM layer network of twice smaller size
+ graph = lstm(graph, lstm_cell_size/2, num_time_steps=num_time_steps, batch_size=batch_size,
+ output_activation_size=num_classes, scope='lstm2', name='sync_cell_l2',
+ weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse,
+ dropout=dropout, input_dropout=input_dropout, output_dropout=output_dropout, full_output=True)
+
+ # Third LSTM layer network three time smaller size
+ graph = lstm(graph, lstm_cell_size/4, num_time_steps=num_time_steps, batch_size=batch_size,
+ output_activation_size=num_classes, scope='lstm3', name='sync_cell_l3',
+ weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse,
+ dropout=dropout, input_dropout=input_dropout, output_dropout=output_dropout)
+
+ regularizer = None
+ # fully connect the LSTM output to the classes
+ graph = slim.fully_connected(graph, num_classes, activation_fn=None, scope='fc1',
+ weights_initializer=initializer, weights_regularizer=regularizer, reuse=reuse)
+
+ return graph