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'])