# -*- coding: utf-8 -*-
from ztlearn.utils import *
from ztlearn.dl.models import Sequential
from ztlearn.dl.optimizers import register_opt
from ztlearn.dl.layers import LSTM, Flatten, Dense
text = open('../../data/text/tinyshakespeare.txt').read().lower()
x, y, len_chars = gen_char_sequence_xtym(text, maxlen=30, step=1)
del text
train_data, test_data, train_label, test_label = train_test_split(
x, y, test_size=0.4)
opt = register_opt(optimizer_name='rmsprop', momentum=0.1, learning_rate=0.01)
# Model definition
model = Sequential()
model.add(LSTM(128, activation='tanh', input_shape=(30, len_chars)))
model.add(Flatten())
model.add(Dense(len_chars, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=opt)
model_epochs = 2
fit_stats = model.fit(train_data,
train_label,
batch_size=128,
epochs=model_epochs,
validation_data=(test_data, test_label))
plot_metric('Loss', model_epochs, fit_stats['train_loss'],
fit_stats['valid_loss'])
from sklearn import datasets
from ztlearn.utils import *
from ztlearn.dl.models import Sequential
from ztlearn.dl.optimizers import register_opt
from ztlearn.dl.layers import BatchNormalization, Conv2D
from ztlearn.dl.layers import Dropout, Dense, Flatten, MaxPooling2D
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())
latent_dim = 100
batch_size = 128
half_batch = int(batch_size * 0.5)
verbose = True
init_type = 'he_uniform'
model_epochs = 7500
model_stats = {
'd_train_loss': [],
'd_train_acc': [],
'g_train_loss': [],
'g_train_acc': []
}
d_opt = register_opt(optimizer_name='adam', beta1=0.5, learning_rate=0.0001)
g_opt = register_opt(optimizer_name='adam', beta1=0.5, learning_rate=0.00001)
def stack_generator_layers(init):
model = Sequential(init_method=init)
model.add(Dense(128, input_shape=(latent_dim, )))
model.add(Activation('relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(img_dim, activation='tanh'))
