Я пытался написать свой собственный LSTM для настройки. Однако произошла ошибка при попытке вызвать мой код с помощью Keras. Ошибка сказала, что график был отключен на c_prev, но c_prev использовался как инициализатор ячейки LSTM. Поэтому я не уверен, что с моим кодом что-то не так или как я называю модель. Любая помощь приветствуется.
Моя среда:
- Python 3.7.6
- Tensorflow 2.1.0 (устанавливается через pip)
- Ма c Мохаве
class EtienneLSTM(tf.keras.layers.Layer):
def __init__(self, units, activation='tanh', recurrent_activation='sigmoid',
kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros',
use_bias=True, unit_forget_bias=True,
kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, recurrent_constraint=None, bias_constraint=None,
# dropout=0.0, recurrent_dropout=0.0,
return_sequences=False, return_state=False, go_backwards=False, use_batchnorm=False):
super(EtienneLSTM, self).__init__()
self.units = units #
self.activation = tf.keras.layers.Activation(activation) #
self.recurrent_activation = tf.keras.layers.Activation(recurrent_activation) #
self.use_bias = use_bias #
self.kernel_initializer = kernel_initializer #
self.recurrent_initializer = recurrent_initializer #
self.bias_initializer = bias_initializer #
self.unit_forget_bias = unit_forget_bias #
if self.unit_forget_bias:
self.bias_initializer = 'zeros'
self.kernel_regularizer = kernel_regularizer #
self.recurrent_regularizer = recurrent_regularizer #
self.bias_regularizer = bias_regularizer #
self.activity_regularizer = activity_regularizer
self.kernel_constraint = kernel_constraint #
self.recurrent_constraint = recurrent_constraint #
self.bias_constraint = bias_constraint #
# self.dropout = dropout
# self.recurrent_dropout = recurrent_dropout
self.return_sequences = return_sequences #
self.return_state = return_state #
self.go_backwards = go_backwards #
self.use_batchnorm = use_batchnorm
if self.use_batchnorm:
self.batchnorm_f = tf.keras.layers.BatchNormalization()
self.batchnorm_i = tf.keras.layers.BatchNormalization()
self.batchnorm_o = tf.keras.layers.BatchNormalization()
self.batchnorm_c = tf.keras.layers.BatchNormalization()
def build(self, input_shape):
# forgot gate
self.Wf = self.add_weight(shape=(input_shape[-1], self.units), initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, trainable=True)
self.Uf = self.add_weight(shape=(self.units, self.units), initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, trainable=True)
if self.unit_forget_bias:
self.bf = self.add_weight(shape=(self.units,), initializer='ones', regularizer=self.bias_regularizer, constraint=self.bias_constraint, trainable=True)
else:
self.bf = self.add_weight(shape=(self.units,), initializer=self.bias_initializer, regularizer=self.bias_regularizer, trainable=True)
# input gate
self.Wi = self.add_weight(shape=(input_shape[-1], self.units), initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, trainable=True)
self.Ui = self.add_weight(shape=(self.units, self.units), initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, trainable=True)
if self.use_bias:
self.bi = self.add_weight(shape=(self.units,), initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint, trainable=True)
# output gate
self.Wo = self.add_weight(shape=(input_shape[-1], self.units), initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, trainable=True)
self.Uo = self.add_weight(shape=(self.units, self.units), initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, trainable=True)
if self.use_bias:
self.bo = self.add_weight(shape=(self.units,), initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint, trainable=True)
# context
self.Wc = self.add_weight(shape=(input_shape[-1], self.units), initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint, trainable=True)
self.Uc = self.add_weight(shape=(self.units, self.units), initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint, trainable=True)
if self.use_bias:
self.bc = self.add_weight(shape=(self.units,), initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint, trainable=True)
def _inp_gate(self, x, hidden):
return self.recurrent_activation(tf.matmul(x, self.Wi) + tf.matmul(hidden, self.Ui) + self.bi)
def _new_mem(self, x, hidden):
return self.activation(tf.matmul(x, self.Wc) + tf.matmul(hidden, self.Uc) + self.bc)
def _forget_gate(self, x, hidden):
return self.recurrent_activation(tf.matmul(x, self.Wf) + tf.matmul(hidden, self.Uf) + self.bf)
def _update_cell(self, c_prev, c_tilde, f_t, i_t):
return (f_t * c_prev) + (i_t * c_tilde)
def _out_gate(self, x, hidden, ct):
ot = self.recurrent_activation(tf.matmul(x, self.Wo) + tf.matmul(hidden, self.Uo) + self.bo)
return ot * self.activation(ct)
def call(self, x, hidden, c_prev):
if self.go_backwards: x = x[:,:,::-1]
f_t = self._forget_gate(x, hidden)
i_t = self._inp_gate(x, hidden)
c_tilde = self._new_mem(x, hidden)
c_t = self._update_cell(c_prev, c_tilde, f_t, i_t)
h_t = self._out_gate(x, hidden, c_t)
# if self.return_state:
# return h_t, c_t
# if self.return_sequences:
# return h_t
return h_t
tf.keras.backend.clear_session()
def get_LSTM():
inp = tf.keras.layers.Input(shape=(200, 40))
out = tf.keras.layers.LSTM(32)(inp)
return tf.keras.Model(inp, out)
def get_EtienneLSTM():
inp = tf.keras.layers.Input(shape=(200, 40))
h0 = tf.keras.layers.Input(shape=(32,), name='h0')
c0 = tf.keras.layers.Input(shape=(32,), name='c0')
out = EtienneLSTM(32)(inp, h0, c0)
return tf.keras.Model(inp, out)
model_tf = get_LSTM()
model_etienne = get_EtienneLSTM()
Вот мое сообщение об ошибке:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in
14
15 model_tf = get_LSTM()
---> 16 model_etienne = get_EtienneLSTM()
in get_EtienneLSTM()
11 c0 = tf.keras.layers.Input(shape=(32,), name='c0')
12 out = EtienneLSTM(32)(inp, h0, c0)
---> 13 return tf.keras.Model(inp, out)
14
15 model_tf = get_LSTM()
~/.env/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in __init__(self, *args, **kwargs)
144
145 def __init__(self, *args, **kwargs):
--> 146 super(Model, self).__init__(*args, **kwargs)
147 _keras_api_gauge.get_cell('model').set(True)
148 # initializing _distribution_strategy here since it is possible to call
~/.env/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in __init__(self, *args, **kwargs)
167 'inputs' in kwargs and 'outputs' in kwargs):
168 # Graph network
--> 169 self._init_graph_network(*args, **kwargs)
170 else:
171 # Subclassed network
~/.env/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/.env/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
322 # Keep track of the network's nodes and layers.
323 nodes, nodes_by_depth, layers, _ = _map_graph_network(
--> 324 self.inputs, self.outputs)
325 self._network_nodes = nodes
326 self._nodes_by_depth = nodes_by_depth
~/.env/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in _map_graph_network(inputs, outputs)
1674 'The following previous layers '
1675 'were accessed without issue: ' +
-> 1676 str(layers_with_complete_input))
1677 for x in nest.flatten(node.output_tensors):
1678 computable_tensors.add(id(x))
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("c0:0", shape=(None, 32), dtype=float32) at layer "c0". The following previous layers were accessed without issue: ['input_2']
Спасибо за вашу помощь.