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))