text = open(
'../../../ztlearn/datasets/text/nietzsche_short.txt').read().lower()
x, y, len_chars = gen_char_sequence_xtyt(text, maxlen=30, step=1)
del text
train_data, test_data, train_label, test_label = train_test_split(
x, y, test_size=0.4)
# optimizer definition
opt = register_opt(optimizer_name='rmsprop', momentum=0.1, learning_rate=0.01)
# model definition
model = Sequential()
model.add(GRU(128, activation='tanh', input_shape=(30, len_chars)))
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary('nietzsche gru')
model_epochs = 20
fit_stats = model.fit(train_data,
train_label,
batch_size=128,
epochs=model_epochs,
validation_data=(test_data, test_label),
verbose=False)
model_name = model.model_name
plot_metric('loss',
model_epochs,
fit_stats['train_loss'],
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')
model_epochs = 12 # change to 200 epochs
fit_stats = model.fit(reshaped_train_data,
one_hot(train_label),
batch_size = 128,
epochs = model_epochs,
validation_data = (reshaped_test_data, one_hot(test_label)),
shuffle_data = True)
eval_stats = model.evaluate(reshaped_test_data, one_hot(test_label))
predictions = unhot(model.predict(reshaped_test_data, True))
print_results(predictions, test_label)
return model
# stack and compile the generator
generator = stack_generator_layers(init=init_type)
generator.compile(loss='cce', optimizer=g_opt)
# stack and compile the discriminator
discriminator = stack_discriminator_layers(init=init_type)
discriminator.compile(loss='cce', optimizer=d_opt)
# stack and compile the generator_discriminator
generator_discriminator = Sequential(init_method=init_type)
generator_discriminator.layers.extend(generator.layers)
generator_discriminator.layers.extend(discriminator.layers)
generator_discriminator.compile(loss='cce', optimizer=g_opt)
generator.summary('digits generator')
discriminator.summary('digits discriminator')
generator_discriminator.summary('digits gan')
model_name = generator_discriminator.model_name
# rescale to range [-1, 1]
images = range_normalize(data.data.astype(np.float32))
for epoch_idx in range(model_epochs):
# set the epoch id for print out
print_epoch = epoch_idx + 1
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(img_dim, activation='sigmoid'))
return model
encoder = stack_encoder_layers(init=init_type)
decoder = stack_decoder_layers(init=init_type)
opt = register_opt(optimizer_name='adam', momentum=0.01, lr=0.001)
autoencoder = Sequential(init_method=init_type)
autoencoder.layers.extend(encoder.layers)
autoencoder.layers.extend(decoder.layers)
autoencoder.compile(loss='categorical_crossentropy', optimizer=opt)
encoder.summary('cifar-10 encoder')
decoder.summary('cifar-10 decoder')
autoencoder.summary('cifar-10 autoencoder')
data = fetch_cifar_10()
train_data, test_data, train_label, test_label = train_test_split(
data.data, data.data, test_size=0.2, random_seed=5, cut_off=2000)
# plot samples of training data
plot_img_samples(train_data, None, dataset='cifar', channels=3)
transformed_image_dims = img_dim
transformed_train_data = z_score(
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))
predictions = unhot(model.predict(test_data, True))
print_results(predictions, test_label)
