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build_model_basic.py
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build_model_basic.py
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import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.python.ops import variable_scope
from tensorflow.python.framework import dtypes
import copy
## Parameters
learning_rate = 0.01
lambda_l2_reg = 0.003
## Network Parameters
# length of input signals
input_seq_len = 15
# length of output signals
output_seq_len = 20
# size of LSTM Cell
hidden_dim = 64
# num of input signals
input_dim = 1
# num of output signals
output_dim = 1
# num of stacked lstm layers
num_stacked_layers = 2
# gradient clipping - to avoid gradient exploding
GRADIENT_CLIPPING = 2.5
def build_graph(feed_previous = False):
tf.reset_default_graph()
global_step = tf.Variable(
initial_value=0,
name="global_step",
trainable=False,
collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES])
weights = {
'out': tf.get_variable('Weights_out', \
shape = [hidden_dim, output_dim], \
dtype = tf.float32, \
initializer = tf.truncated_normal_initializer()),
}
biases = {
'out': tf.get_variable('Biases_out', \
shape = [output_dim], \
dtype = tf.float32, \
initializer = tf.constant_initializer(0.)),
}
with tf.variable_scope('Seq2seq'):
# Encoder: inputs
enc_inp = [
tf.placeholder(tf.float32, shape=(None, input_dim), name="inp_{}".format(t))
for t in range(input_seq_len)
]
# Decoder: target outputs
target_seq = [
tf.placeholder(tf.float32, shape=(None, output_dim), name="y".format(t))
for t in range(output_seq_len)
]
# Give a "GO" token to the decoder.
# If dec_inp are fed into decoder as inputs, this is 'guided' training; otherwise only the
# first element will be fed as decoder input which is then 'un-guided'
dec_inp = [ tf.zeros_like(target_seq[0], dtype=tf.float32, name="GO") ] + target_seq[:-1]
with tf.variable_scope('LSTMCell'):
cells = []
for i in range(num_stacked_layers):
with tf.variable_scope('RNN_{}'.format(i)):
cells.append(tf.contrib.rnn.LSTMCell(hidden_dim))
cell = tf.contrib.rnn.MultiRNNCell(cells)
def _rnn_decoder(decoder_inputs,
initial_state,
cell,
loop_function=None,
scope=None):
"""RNN decoder for the sequence-to-sequence model.
Args:
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
initial_state: 2D Tensor with shape [batch_size x cell.state_size].
cell: rnn_cell.RNNCell defining the cell function and size.
loop_function: If not None, this function will be applied to the i-th output
in order to generate the i+1-st input, and decoder_inputs will be ignored,
except for the first element ("GO" symbol). This can be used for decoding,
but also for training to emulate http://arxiv.org/abs/1506.03099.
Signature -- loop_function(prev, i) = next
* prev is a 2D Tensor of shape [batch_size x output_size],
* i is an integer, the step number (when advanced control is needed),
* next is a 2D Tensor of shape [batch_size x input_size].
scope: VariableScope for the created subgraph; defaults to "rnn_decoder".
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x output_size] containing generated outputs.
state: The state of each cell at the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
(Note that in some cases, like basic RNN cell or GRU cell, outputs and
states can be the same. They are different for LSTM cells though.)
"""
with variable_scope.variable_scope(scope or "rnn_decoder"):
state = initial_state
outputs = []
prev = None
for i, inp in enumerate(decoder_inputs):
if loop_function is not None and prev is not None:
with variable_scope.variable_scope("loop_function", reuse=True):
inp = loop_function(prev, i)
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
output, state = cell(inp, state)
outputs.append(output)
if loop_function is not None:
prev = output
return outputs, state
def _basic_rnn_seq2seq(encoder_inputs,
decoder_inputs,
cell,
feed_previous,
dtype=dtypes.float32,
scope=None):
"""Basic RNN sequence-to-sequence model.
This model first runs an RNN to encode encoder_inputs into a state vector,
then runs decoder, initialized with the last encoder state, on decoder_inputs.
Encoder and decoder use the same RNN cell type, but don't share parameters.
Args:
encoder_inputs: A list of 2D Tensors [batch_size x input_size].
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
feed_previous: Boolean; if True, only the first of decoder_inputs will be
used (the "GO" symbol), all other inputs will be generated by the previous
decoder output using _loop_function below. If False, decoder_inputs are used
as given (the standard decoder case).
dtype: The dtype of the initial state of the RNN cell (default: tf.float32).
scope: VariableScope for the created subgraph; default: "basic_rnn_seq2seq".
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x output_size] containing the generated outputs.
state: The state of each decoder cell in the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
"""
with variable_scope.variable_scope(scope or "basic_rnn_seq2seq"):
enc_cell = copy.deepcopy(cell)
_, enc_state = rnn.static_rnn(enc_cell, encoder_inputs, dtype=dtype)
if feed_previous:
return _rnn_decoder(decoder_inputs, enc_state, cell, _loop_function)
else:
return _rnn_decoder(decoder_inputs, enc_state, cell)
def _loop_function(prev, _):
'''Naive implementation of loop function for _rnn_decoder. Transform prev from
dimension [batch_size x hidden_dim] to [batch_size x output_dim], which will be
used as decoder input of next time step '''
return tf.matmul(prev, weights['out']) + biases['out']
dec_outputs, dec_memory = _basic_rnn_seq2seq(
enc_inp,
dec_inp,
cell,
feed_previous = feed_previous
)
reshaped_outputs = [tf.matmul(i, weights['out']) + biases['out'] for i in dec_outputs]
# Training loss and optimizer
with tf.variable_scope('Loss'):
# L2 loss
output_loss = 0
for _y, _Y in zip(reshaped_outputs, target_seq):
output_loss += tf.reduce_mean(tf.pow(_y - _Y, 2))
# L2 regularization for weights and biases
reg_loss = 0
for tf_var in tf.trainable_variables():
if 'Biases_' in tf_var.name or 'Weights_' in tf_var.name:
reg_loss += tf.reduce_mean(tf.nn.l2_loss(tf_var))
loss = output_loss + lambda_l2_reg * reg_loss
with tf.variable_scope('Optimizer'):
optimizer = tf.contrib.layers.optimize_loss(
loss=loss,
learning_rate=learning_rate,
global_step=global_step,
optimizer='Adam',
clip_gradients=GRADIENT_CLIPPING)
saver = tf.train.Saver
return dict(
enc_inp = enc_inp,
target_seq = target_seq,
train_op = optimizer,
loss=loss,
saver = saver,
reshaped_outputs = reshaped_outputs,
)