| import tensorflow as tf |
| import numpy as np |
|
|
| from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, ZeroPadding1D,\ |
| Flatten, BatchNormalization, AveragePooling1D, Dense, Activation, Add, Softmax, Reshape |
| from tensorflow.keras.models import Model |
| from tensorflow.keras import activations |
| from tensorflow.keras.activations import softmax |
| from tensorflow.keras.optimizers import Adam |
| from tensorflow.keras.callbacks import EarlyStopping |
| from tensorflow.keras.regularizers import l2 |
|
|
| from tensorflow.keras.initializers import RandomUniform |
|
|
| import tensorflow.keras.backend as K |
|
|
| def resblock2(inputs,num_channels): |
| res = inputs |
| |
| c = num_channels |
| |
| x = Activation(activations.relu)(res) |
| x = Conv1D(c, kernel_size=5, strides=1, padding='same', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(x) |
| |
|
|
| x = Activation(activations.relu)(x) |
| x = Conv1D(c, kernel_size=5, strides=1, padding='same', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001))(x) |
| |
| |
| |
| x = Add()([x * 0.3, inputs]) |
| return x |
|
|
| def resnet_g2(dim, num_channels, seq_len, vocab_size, annotated=False, res_layers=2, batch_size=64): |
| output_size = seq_len * num_channels |
| input_data = Input(shape=(dim)) |
|
|
| x = input_data |
| x = Dense(output_size,kernel_regularizer=l2(0.01),bias_regularizer=l2(0.001))(x) |
|
|
| x = Reshape(target_shape=(-1,num_channels))(x) |
| |
| for layer in range(res_layers): |
| x = resblock2(x,num_channels) |
|
|
| x = Conv1D(vocab_size,1,padding='same', kernel_regularizer=l2(0.01),bias_regularizer=l2(0.01))(x) |
|
|
| x = Softmax()(x) |
| |
|
|
| model = Model(inputs=input_data, outputs=x, name='Generator') |
|
|
| return model |
|
|
| def resnet_d2(num_channels, seq_len, vocab_size, batch_size=64, res_layers=2): |
| input_size = seq_len * vocab_size |
| input_data = Input(shape=(seq_len,vocab_size)) |
|
|
| x = input_data |
| x = Conv1D(num_channels,kernel_size=1,padding='same')(x) |
| |
| for layer in range(res_layers): |
| x = resblock2(x,num_channels) |
|
|
| x = tf.keras.layers.Flatten()(x) |
|
|
| x = Dense(1)(x) |
|
|
| model = Model(inputs=input_data, outputs=x, name='Discriminator') |
|
|
| return model |
|
|
| def wasserstein_loss( y_true, y_pred): |
| return K.mean(y_true * y_pred) |
|
|
| def gradient_penalty_loss(y_true, y_pred, averaged_samples): |
| """ |
| Computes gradient penalty based on prediction and weighted real / fake samples |
| """ |
| gradients = K.gradients(y_pred, averaged_samples)[0] |
| |
| gradients_sqr = K.square(gradients) |
| |
| gradients_sqr_sum = K.sum(gradients_sqr, |
| axis=np.arange(1, len(gradients_sqr.shape))) |
| |
| gradient_l2_norm = K.sqrt(gradients_sqr_sum) |
| |
| gradient_penalty = K.square(1 - gradient_l2_norm) |
| |
| return K.mean(gradient_penalty) |
