def _build_model(self):
if self.network == CNNNetwork.CNN5:
x = CNN5(inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNet:
x = ResNet50(inputs=self.inputs, utils=self.utils).build()
else:
print('This cnn neural network is not supported at this time.')
sys.exit(-1)
shape_list = x.get_shape().as_list()
self.seq_len = tf.fill([tf.shape(x)[0]], shape_list[1], name="seq_len")
if self.recurrent == RecurrentNetwork.LSTM:
recurrent_network_builder = LSTM(self.utils, x, self.seq_len)
elif self.recurrent == RecurrentNetwork.BLSTM:
recurrent_network_builder = BLSTM(self.utils, x, self.seq_len)
elif self.recurrent == RecurrentNetwork.GRU:
recurrent_network_builder = GRU(x, self.seq_len)
elif self.recurrent == RecurrentNetwork.SRU:
recurrent_network_builder = SRU(x, self.seq_len)
elif self.recurrent == RecurrentNetwork.BSRU:
recurrent_network_builder = BSRU(self.utils, x, self.seq_len)
else:
print(
'This recurrent neural network is not supported at this time.')
sys.exit(-1)
outputs = recurrent_network_builder.build()
# Reshaping to apply the same weights over the time_steps
outputs = tf.reshape(outputs, [-1, NUM_HIDDEN * 2])
with tf.variable_scope('output'):
# tf.Variable
weight_out = tf.get_variable(
name='weight',
shape=[
outputs.get_shape()[1] if self.network == CNNNetwork.ResNet
else NUM_HIDDEN * 2, NUM_CLASSES
],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1),
# initializer=tf.glorot_uniform_initializer(),
# initializer=tf.contrib.layers.xavier_initializer(),
# initializer=tf.truncated_normal([NUM_HIDDEN, NUM_CLASSES], stddev=0.1),
)
biases_out = tf.get_variable(name='biases',
shape=[NUM_CLASSES],
dtype=tf.float32,
initializer=tf.constant_initializer(
value=0, dtype=tf.float32))
# [batch_size * max_timesteps, num_classes]
logits = tf.matmul(outputs, weight_out) + biases_out
# Reshaping back to the original shape
logits = tf.reshape(logits, [tf.shape(x)[0], -1, NUM_CLASSES])
# Time major
predict = tf.transpose(logits, (1, 0, 2), "predict")
self.predict = predict
def _build_model(self):
"""选择采用哪种卷积网络"""
if self.network == CNNNetwork.CNN5:
x = CNN5(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.CNNX:
x = CNNX(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNetTiny:
x = ResNetTiny(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNet50:
x = ResNet50(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.DenseNet:
x = DenseNet(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
else:
raise ValueError('This cnn neural network is not supported at this time.')
"""选择采用哪种循环网络"""
# time_major = True: [max_time_step, batch_size, num_classes]
tf.compat.v1.logging.info("CNN Output: {}".format(x.get_shape()))
self.seq_len = tf.fill([tf.shape(x)[0]], tf.shape(x)[1], name="seq_len")
# self.labels_len = tf.fill([BATCH_SIZE], 12, name="labels_len")
if self.recurrent == RecurrentNetwork.NoRecurrent:
self.recurrent_network_builder = None
elif self.recurrent == RecurrentNetwork.LSTM:
self.recurrent_network_builder = LSTM(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTM:
self.recurrent_network_builder = BiLSTM(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRU:
self.recurrent_network_builder = GRU(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiGRU:
self.recurrent_network_builder = BiGRU(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.LSTMcuDNN:
self.recurrent_network_builder = LSTMcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTMcuDNN:
self.recurrent_network_builder = BiLSTMcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRUcuDNN:
self.recurrent_network_builder = GRUcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
else:
raise ValueError('This recurrent neural network is not supported at this time.')
logits = self.recurrent_network_builder.build() if self.recurrent_network_builder else x
if self.recurrent_network_builder and self.model_conf.loss_func != LossFunction.CTC:
raise ValueError('CTC loss must use recurrent neural network.')
"""输出层，根据Loss函数区分"""
with tf.keras.backend.name_scope('output'):
if self.model_conf.loss_func == LossFunction.CTC:
self.outputs = FullConnectedRNN(model_conf=self.model_conf, mode=self.mode, outputs=logits).build()
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.outputs = FullConnectedCNN(model_conf=self.model_conf, mode=self.mode, outputs=logits).build()
return self.outputs