from ztlearn.datasets.digits import fetch_digits
from ztlearn.dl.layers import LSTM, Dense, Flatten
data = fetch_digits()
train_data, test_data, train_label, test_label = train_test_split(
data.data, data.target, test_size=0.3, random_seed=15)
# plot samples of training data
plot_img_samples(train_data, train_label)
# optimizer definition
opt = register_opt(optimizer_name='adam', momentum=0.01, learning_rate=0.001)
# Model definition
model = Sequential()
model.add(LSTM(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 lstm')
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)
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'])
plot_metric('Accuracy', model_epochs, fit_stats['train_acc'],
fit_stats['valid_acc'])
from ztlearn.utils import *
from ztlearn.dl.layers import LSTM
from ztlearn.dl.models import Sequential
from ztlearn.dl.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)
print_seq_samples(train_data, train_label)
opt = register_opt(optimizer_name = 'adagrad', momentum = 0.01, learning_rate = 0.01)
# Model definition
model = Sequential()
model.add(LSTM(10, activation = 'tanh', input_shape = (10, seq_len)))
model.compile(loss = 'categorical_crossentropy', optimizer = opt)
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)
plot_metric('Loss', model_epochs, fit_stats['train_loss'], fit_stats['valid_loss'])
plot_metric('Accuracy', model_epochs, fit_stats['train_acc'], fit_stats['valid_acc'])
