class Extractor_Model(tf.keras.Model):
def __init__(self, embedding_matrix):
super(Extractor_Model, self).__init__()
#hyperparameters
num_filters = 100
sequence_length = 540
embedding_dimension = 100
num_words = 4860
#model
self.model = Sequential()
self.model.add(layers.Embedding(input_dim=num_words, output_dim=embedding_dimension,
embeddings_initializer=initializers.Constant(embedding_matrix),
input_length=sequence_length, trainable=False))
self.conv_2 = layers.Conv1D(filters=num_filters, kernel_size=2, padding='same', activation='relu')
self.conv_3 = layers.Conv1D(filters=num_filters, kernel_size=3, padding='same', activation='relu')
self.conv_4 = layers.Conv1D(filters=num_filters, kernel_size=4, padding='same', activation='relu')
self.conv_5 = layers.Conv1D(filters=num_filters, kernel_size=5, padding='same', activation='relu')
self.conv_6 = layers.Conv1D(filters=num_filters, kernel_size=6, padding='same', activation='relu')
self.global_max_pool = layers.GlobalMaxPooling1D()
#optimizer
learning_rate = 0.001
decay_rate = learning_rate/((1 + 10 * np.random.randint(0, 2)) ** 0.75)
self.optimizer = tf.keras.optimizers.Adam(lr=learning_rate, decay=decay_rate)
@tf.function
def call(self, inputs):
embedded_sequences = self.model.call(inputs)
conv_2 = self.conv_2(embedded_sequences)
conv_3 = self.conv_3(embedded_sequences)
conv_4 = self.conv_4(embedded_sequences)
conv_5 = self.conv_5(embedded_sequences)
conv_6 = self.conv_6(embedded_sequences)
conv_2 = self.global_max_pool(conv_2)
conv_3 = self.global_max_pool(conv_3)
conv_4 = self.global_max_pool(conv_4)
conv_5 = self.global_max_pool(conv_5)
conv_6 = self.global_max_pool(conv_6)
concat = layers.concatenate([conv_2, conv_3, conv_4, conv_5, conv_6])
return concat
@tf.function
def loss_function(self, detector, discriminator):
return detector + discriminator
DATA_WIDTH = len(dataSource.tags_list)
NUM_OF_FRAMES = data.shape[0]-FRAME_SIZE-1
X = np.zeros((NUM_OF_FRAMES, FRAME_SIZE, DATA_WIDTH), dtype=np.float64)
Y = np.zeros((NUM_OF_FRAMES, DATA_WIDTH), dtype=np.float64)
for frame_ in range(NUM_OF_FRAMES):
X[frame_] = data[frame_:frame_+FRAME_SIZE, ...]
Y[frame_] = data[frame_+FRAME_SIZE+1]
model = Sequential()
model.add(Dense(100, input_shape = (FRAME_SIZE, DATA_WIDTH)))
model.add(Activation('relu'))
model.add(LSTM(57, return_sequences=True))
model.add(LSTM(DATA_WIDTH*2))
model.add(Dropout(0.5))
model.add(Dense(DATA_WIDTH))
model.add(Activation('sigmoid'))
model.compile(optimizer='rmsprop',
loss='mse')
model.fit(X, Y, )
A = model.call(X)
print( 1/A )
pass
