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import tensorflow as tf
import parameters as params
import os
class bidirectional_model(tf.keras.Model):
def __init__(self, num_bidirectional_layers=params.NUM_GRU_LAYERS, width=params.GRU_WIDTH):
super(bidirectional_model, self).__init__()
self.bidirectional_layers = [
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(width, return_sequences=True))
for _ in range(num_bidirectional_layers//2)
]
for _ in range(num_bidirectional_layers//2, num_bidirectional_layers):
self.bidirectional_layers.append(tf.keras.layers.Bidirectional(tf.keras.layers.GRU(width, return_sequences=True)))
self.bidirectional_layers.append(tf.keras.layers.Dropout(0.2))
self.final_layer = tf.keras.layers.Dense(1, activation='sigmoid')
def call(self, x, training=False):
for layer in self.bidirectional_layers:
x = layer(x, training=training)
return tf.reshape(self.final_layer(x), [self.final_layer(x).shape[0], self.final_layer(x).shape[1]])
class bidirectional_model_for_save(tf.keras.Model):
def __init__(self, num_bidirectional_layers=params.NUM_GRU_LAYERS, width=params.GRU_WIDTH):
super(bidirectional_model_for_save, self).__init__()
self.bidirectional_layers = [
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(params.GRU_WIDTH, return_sequences=True))
for _ in range(num_bidirectional_layers)
]
self.final_layer = tf.keras.layers.Dense(1, activation='sigmoid')
def call(self, x, data_aug=True):
for layer in self.bidirectional_layers:
x = layer(x)
return self.final_layer(x)
def save_weights(model, model_save, name_saving_folder):
"""
To save the weights of the model. As in the last layer there is a reshape, doing model.save() directly
gives an error.
In order for save_weights not to give an error, model_save has to be used at least once, to set the sizes of its weights.
"""
weights = model.get_weights()
model_save.set_weights(weights)
os.makedirs(name_saving_folder)
model_save.save_weights(name_saving_folder+'/'+name_saving_folder)
del weights
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