def stack_discriminator_layers(init): model = Sequential(init_method=init) model.add(Dense(256, input_shape=(img_dim, ))) model.add(Activation('leaky_relu', alpha=0.2)) model.add(Dropout(0.25)) model.add(Dense(128)) model.add(Activation('leaky_relu', alpha=0.2)) model.add(Dropout(0.25)) model.add(Dense(2, activation='sigmoid')) return model
def stack_discriminator_layers(init): model = Sequential(init_method=init) model.add( Conv2D(64, kernel_size=(5, 5), padding='same', input_shape=img_dims)) model.add(Activation('leaky_relu')) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=(5, 5), padding='same')) model.add(Activation('leaky_relu')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(2)) model.add(Activation('sigmoid')) return model
data = datasets.load_digits() plot_digits_img_samples(data) train_data, test_data, train_label, test_label = train_test_split( data.data, data.target, test_size=0.33, random_seed=5) opt = register_opt(optimizer_name='adam', momentum=0.01, learning_rate=0.001) model = Sequential(init_method='he_uniform') model.add( Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(1, 8, 8), padding='same')) model.add(Dropout(0.25)) model.add(BatchNormalization()) model.add( Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(BatchNormalization()) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(BatchNormalization()) model.add(Dense(10, activation='softmax')) # 10 digits classes model.compile(loss='categorical_crossentropy', optimizer=opt) model_epochs = 12 fit_stats = model.fit(train_data.reshape(-1, 1, 8, 8),
random_seed = 3) # plot samples of training data plot_img_samples(train_data, train_label, dataset = 'cifar', channels = 3) reshaped_image_dims = 3 * 32 * 32 # ==> (channels * height * width) reshaped_train_data = z_score(train_data.reshape(train_data.shape[0], reshaped_image_dims).astype('float32')) reshaped_test_data = z_score(test_data.reshape(test_data.shape[0], reshaped_image_dims).astype('float32')) # optimizer definition opt = register_opt(optimizer_name = 'adam', momentum = 0.01, lr = 0.0001) model = Sequential() model.add(Dense(1024, input_shape = (3072, ))) model.add(Activation('relu')) model.add(Dropout(0.2)) model.add(BatchNormalization()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.2)) model.add(BatchNormalization()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.2)) model.add(BatchNormalization()) model.add(Dense(100)) model.add(Activation('softmax')) model.compile(loss = 'cce', optimizer = opt) model.summary(model_name = 'cifar-100 mlp')
from ztlearn.dl.layers import Dropout, Dense, BatchNormalization mnist = fetch_mnist() train_data, test_data, train_label, test_label = train_test_split(mnist.data, mnist.target.astype('int'), test_size = 0.33, random_seed = 5, cut_off = 2000) # plot samples of training data plot_tiled_img_samples(train_data[:40], train_label[:40], dataset = 'mnist') # model definition model = Sequential() model.add(Dense(512, activation = 'relu', input_shape = (784,))) model.add(Dropout(0.3)) model.add(BatchNormalization()) model.add(Dense(10, activation = 'relu')) # 10 digits classes model.compile(loss = 'cce', optimizer = Adam()) model.summary() model_epochs = 12 fit_stats = model.fit(train_data, one_hot(train_label), batch_size = 128, epochs = model_epochs, validation_data = (test_data, one_hot(test_label)), shuffle_data = True) eval_stats = model.evaluate(test_data, one_hot(test_label))