from ztlearn.optimizers import register_opt
x, y, seq_len = gen_mult_sequence_xtyt(1000, 10, 10)
train_data, test_data, train_label, test_label = train_test_split(
x, y, test_size=0.4)
# plot samples of training data
print_seq_samples(train_data, train_label)
# optimizer definition
opt = register_opt(optimizer_name='rmsprop', momentum=0.01, learning_rate=0.01)
# opt = register_opt(optimizer_name = 'adadelta', momentum = 0.01, learning_rate = 1)
# model definition
model = Sequential()
model.add(GRU(10, activation='tanh', input_shape=(10, seq_len)))
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary('seq gru')
model_epochs = 100
fit_stats = model.fit(train_data,
train_label,
batch_size=100,
epochs=model_epochs,
validation_data=(test_data, test_label))
print_seq_results(model.predict(test_data, (0, 2, 1)),
test_label,
test_data,
unhot_axis=2)
from ztlearn.optimizers import register_opt
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,
from ztlearn.datasets.digits import fetch_digits
from ztlearn.dl.layers import GRU, Dense, Flatten
data = fetch_digits()
train_data, test_data, train_label, test_label = train_test_split(
data.data, data.target, test_size=0.33, random_seed=15)
# plot samples of training data
plot_img_samples(train_data, train_label)
# optimizer definition
opt = register_opt(optimizer_name='rmsprop', momentum=0.01, lr=0.001)
# model definition
model = Sequential()
model.add(GRU(128, activation='tanh', input_shape=(8, 8)))
model.add(Flatten())
model.add(Dense(10, activation='softmax')) # 10 digits classes
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary('digits_gru')
model_epochs = 100
fit_stats = model.fit(train_data.reshape(-1, 8, 8),
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(test_data.reshape(-1, 8, 8),
one_hot(test_label)),
shuffle_data=True)
