我已经能够使用以下代码实现无状态模型
import os
os.environ['TF_ENABLE_CONTROL_FLOW_V2'] = '1'
import tensorflow as tf
from tensorflow_core.python.keras.models import Model, Sequential
from tensorflow_core.python.keras.layers.core import Dense, Activation, Lambda, Reshape
from tensorflow_core.python.keras.engine.input_layer import Input
from tensorflow_core.python.keras.layers.recurrent import RNN, StackedRNNCells
from tensorflow_core.lite.experimental.examples.lstm.rnn_cell import TFLiteLSTMCell, TfLiteRNNCell
from tensorflow_core.lite.experimental.examples.lstm.rnn import dynamic_rnn
from tensorflow_core.python.ops.rnn_cell_impl import LSTMStateTuple
def buildRNNLayer(inputs, rnn_cells):
"""Build the lstm layer.
Args:
inputs: The input data.
num_layers: How many LSTM layers do we want.
num_units: The unmber of hidden units in the LSTM cell.
"""
rnn_layers = StackedRNNCells(rnn_cells)
# Assume the input is sized as [batch, time, input_size], then we're going
# to transpose to be time-majored.
transposed_inputs = tf.transpose(inputs, perm=[1, 0, 2])
outputs, _ = dynamic_rnn(
rnn_layers,
transposed_inputs,
dtype='float32',
time_major=True)
unstacked_outputs = tf.unstack(outputs, axis=0)
return unstacked_outputs[-1]
def build_rnn_lite(model):
tf.reset_default_graph()
# Construct RNN
cells = []
for layer in range(3):
if model == 'LSTMLite':
cells.append(TFLiteLSTMCell(192, name='lstm{}'.format(layer)))
else:
cells.append(TfLiteRNNCell(192, name='rnn{}'.format(layer)))
spec_input = Input(shape=(5, 64,), name='rnn_in', batch_size=8192)
x = Lambda(buildRNNLayer, arguments={'rnn_cells': cells}, name=model.lower())(spec_input)
out = Dense(64, activation='sigmoid', name='fin_dense')(x)
return Model(inputs=spec_input, outputs=out)
model = build_rnn_lite('LSTMLite')
###### TF LITE CONVERSION
sess = tf.keras.backend.get_session()
input_tensor = sess.graph.get_tensor_by_name('rnn_in:0')
output_tensor = sess.graph.get_tensor_by_name('fin_dense/Sigmoid:0')
converter = tf.lite.TFLiteConverter.from_session(sess, [input_tensor], [output_tensor])
tflite = converter.convert()
print('Model converted successfully!')
这工作正常，我正在尝试创建一个有状态的模型，即通过更改下面的代码将以前的状态与输入一起馈送
def buildRNNLayer(inputs, rnn_cells, initial_state=None):
"""Build the lstm layer.
Args:
inputs: The input data.
num_layers: How many LSTM layers do we want.
num_units: The unmber of hidden units in the LSTM cell.
"""
# Assume the input is sized as [batch, time, input_size], then we're going
# to transpose to be time-majored.
transposed_inputs = tf.transpose(inputs, perm=[1, 0, 2])
outputs, new_state = dynamic_rnn(
rnn_cells,
transposed_inputs,
initial_state=initial_state,
dtype='float32',
time_major=True)
unstacked_outputs = tf.unstack(outputs, axis=0)
return unstacked_outputs[-1], new_state
def build_rnn_lite(model, state=False):
tf.reset_default_graph()
# Construct RNN
cells = []
for layer in range(3):
if model == 'LSTMLite':
cells.append(TFLiteLSTMCell(192, name='lstm{}'.format(layer)))
else:
cells.append(TfLiteRNNCell(192, name='rnn{}'.format(layer)))
cells = StackedRNNCells(cells)
state = cells.get_initial_state(batch_size=1, dtype=tf.float32)
if state:
spec_input = Input(shape=(5, 64,), name='rnn_in', batch_size=1)
x, state = Lambda(buildRNNLayer, arguments={'rnn_cells': cells, 'initial_state': state}, name=model.lower())(spec_input)
else:
spec_input = Input(shape=(5, 64,), name='rnn_in')
x, state = Lambda(buildRNNLayer, arguments={'rnn_cells': cells}, name=model.lower())(spec_input)
out = Dense(64, activation='sigmoid', name='fin_dense')(x)
return Model(inputs=spec_input, outputs=[out, state])
model = build_rnn_lite('LSTMLite', True)
in_rnn = np.random.randn(1, 5, 64)
out1 = model.predict(in_rnn)
out2 = model.predict(in_rnn)
###### TF LITE CONVERSION
sess = tf.keras.backend.get_session()
input_tensor = sess.graph.get_tensor_by_name('rnn_in:0')
output_tensor = sess.graph.get_tensor_by_name('fin_dense/Sigmoid:0')
converter = tf.lite.TFLiteConverter.from_session(sess, [input_tensor], [output_tensor])
tflite = converter.convert()
print('Model converted successfully!')
在上面更改的代码中，out1和out2是相同的。如果状态是重用而不是重置，则不应出现这种情况。还需要进行哪些其他更改才能确保输出中的new_state用于下一批而不是重置状态？
def get_state_variables(batch_size, cell):
# For each layer, get the initial state and make a variable out of it
# to enable updating its value.
state_variables = []
for state_c, state_h in cell.zero_state(batch_size, tf.float32):
state_variables.append(tf.contrib.rnn.LSTMStateTuple(
tf.Variable(state_c, trainable=False),
tf.Variable(state_h, trainable=False)))
# Return as a tuple, so that it can be fed to dynamic_rnn as an initial state
return tuple(state_variables)
def get_state_update_op(state_variables, new_states):
# Add an operation to update the train states with the last state tensors
update_ops = []
for state_variable, new_state in zip(state_variables, new_states):
# Assign the new state to the state variables on this layer
update_ops.extend([state_variable[0].assign(new_state[0]),
state_variable[1].assign(new_state[1])])
# Return a tuple in order to combine all update_ops into a single operation.
# The tuple's actual value should not be used.
return tf.tuple(update_ops)
def buildMultiCell(cells):
return MultiRNNCell(cells)
def buildRNNLayer(inputs, rnn_cells, initial_state=None):
"""Build the lstm layer.
Args:
inputs: The input data.
num_layers: How many LSTM layers do we want.
num_units: The unmber of hidden units in the LSTM cell.
"""
# Assume the input is sized as [batch, time, input_size], then we're going
# to transpose to be time-majored.
transposed_inputs = tf.transpose(inputs, perm=[1, 0, 2])
outputs, new_state = dynamic_rnn(
rnn_cells,
transposed_inputs,
initial_state=initial_state,
dtype='float32',
time_major=True)
unstacked_outputs = tf.unstack(outputs, axis=0)
update_op = get_state_update_op(initial_state, new_state)
return unstacked_outputs[-1]
def build_rnn_lite(model, state=False):
tf.reset_default_graph()
# Construct RNN
cells = []
for layer in range(3):
if model == 'LSTMLite':
cells.append(TFLiteLSTMCell(192, name='lstm{}'.format(layer)))
else:
cells.append(TfLiteRNNCell(192, name='rnn{}'.format(layer)))
rnn_cells = Lambda(buildMultiCell, name='multicell')(cells)
states = get_state_variables(1, rnn_cells)
if state:
spec_input = Input(shape=(5, 64,), name='rnn_in', batch_size=1)
x = Lambda(buildRNNLayer, arguments={'rnn_cells': rnn_cells, 'initial_state': states}, name=model.lower())(spec_input)
else:
spec_input = Input(shape=(5, 64,), name='rnn_in')
x = Lambda(buildRNNLayer, arguments={'rnn_cells': rnn_cells}, name=model.lower())(spec_input)
out = Dense(64, activation='sigmoid', name='fin_dense')(x)
return Model(inputs=spec_input, outputs=out)
model = build_rnn_lite('LSTMLite', True)
in_rnn = np.random.randn(1, 5, 64)
out1 = model.predict(in_rnn)
out2 = model.predict(in_rnn)
###### TF LITE CONVERSION
sess = tf.keras.backend.get_session()
input_tensor = sess.graph.get_tensor_by_name('rnn_in:0')
output_tensor = sess.graph.get_tensor_by_name('fin_dense/Sigmoid:0')
converter = tf.lite.TFLiteConverter.from_session(sess, [input_tensor], [output_tensor])
tflite = converter.convert()
print('Model converted successfully!')
通过互联网上的其他示例，我能够获得另一个版本的工作，但新状态也没有在此版本中更新。有谁知道如何解决这个问题？
我想我能够使用以下代码解决它
def get_state_variables(batch_size, cell):
# For each layer, get the initial state and make a variable out of it
# to enable updating its value.
state_variables = []
for state_c, state_h in cell.zero_state(batch_size, tf.float32):
state_variables.append(tf.contrib.rnn.LSTMStateTuple(
tf.Variable(state_c, trainable=False),
tf.Variable(state_h, trainable=False)))
# Return as a tuple, so that it can be fed to dynamic_rnn as an initial state
return tuple(state_variables)
def get_state_update_op(state_variables, new_states):
# Add an operation to update the train states with the last state tensors
update_ops = []
for state_variable, new_state in zip(state_variables, new_states):
# Assign the new state to the state variables on this layer
update_ops.extend([state_variable[0].assign(new_state[0]),
state_variable[1].assign(new_state[1])])
# Return a tuple in order to combine all update_ops into a single operation.
# The tuple's actual value should not be used.
return tf.tuple(update_ops)
def buildMultiCell(cells):
return MultiRNNCell(cells)
def buildRNNLayer(inputs, rnn_cells, initial_state=None):
"""Build the lstm layer.
Args:
inputs: The input data.
num_layers: How many LSTM layers do we want.
num_units: The unmber of hidden units in the LSTM cell.
"""
# Assume the input is sized as [batch, time, input_size], then we're going
# to transpose to be time-majored.
transposed_inputs = tf.transpose(inputs, perm=[1, 0, 2])
outputs, new_state = dynamic_rnn(
rnn_cells,
transposed_inputs,
initial_state=initial_state,
dtype='float32',
time_major=True)
unstacked_outputs = tf.unstack(outputs, axis=0)
# update_op = get_state_update_op(initial_state, new_state)
return unstacked_outputs[-1], new_state
def build_rnn_lite(model, state=False):
tf.reset_default_graph()
# Construct RNN
cells = []
for layer in range(3):
if model == 'LSTMLite':
cells.append(TFLiteLSTMCell(192, name='lstm{}'.format(layer)))
else:
cells.append(TfLiteRNNCell(192, name='rnn{}'.format(layer)))
rnn_cells = Lambda(buildMultiCell, name='multicell')(cells)
states = get_state_variables(1, rnn_cells)
if state:
spec_input = Input(shape=(5, 64,), name='rnn_in', batch_size=1)
x, new_states = Lambda(buildRNNLayer, arguments={'rnn_cells': rnn_cells, 'initial_state': states}, name=model.lower())(spec_input)
updated_states = Lambda(get_state_update_op, arguments={'new_states': new_states})(states)
else:
spec_input = Input(shape=(5, 64,), name='rnn_in')
x, new_states = Lambda(buildRNNLayer, arguments={'rnn_cells': rnn_cells}, name=model.lower())(spec_input)
updated_states = Lambda(get_state_update_op, arguments={'new_states': states})(states)
out = Dense(64, activation='sigmoid', name='fin_dense')(x)
return Model(inputs=spec_input, outputs=[out, updated_states])
model = build_rnn_lite('LSTMLite', True)
in_rnn = np.random.randn(1, 5, 64)
out1 = model.predict(in_rnn)
out2 = model.predict(in_rnn)
###### TF LITE CONVERSION
sess = tf.keras.backend.get_session()
input_tensor = sess.graph.get_tensor_by_name('rnn_in:0')
output_tensor = sess.graph.get_tensor_by_name('fin_dense/Sigmoid:0')
converter = tf.lite.TFLiteConverter.from_session(sess, [input_tensor], [output_tensor])
tflite = converter.convert()
print('Model converted successfully!')
上述代码版本中的updated_states似乎发生了变化，希望正在更新。