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'],
fit_stats['valid_loss'],
model_name=model_name)
plot_metric('accuracy',
model_epochs,
fit_stats['train_acc'],
fit_stats['valid_acc'],
model_name=model_name)
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())
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(10, activation='softmax')) # 10 digits classes
model.compile(loss='categorical_crossentropy', optimizer=opt)
model_epochs = 12
fit_stats = model.fit(train_data.reshape(-1, 1, 8, 8),
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(test_data.reshape(-1, 1, 8, 8),
one_hot(test_label)),
shuffle_data=True)
predictions = unhot(model.predict(test_data.reshape(-1, 1, 8, 8), True))
print_results(predictions, test_label)
plot_digits_img_results(test_data, test_label, predictions)
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'])
transformed_image_dims).astype(np.float32))
transformed_train_label = z_score(
train_label.reshape(train_label.shape[0],
transformed_image_dims).astype(np.float32))
transformed_test_data = z_score(
test_data.reshape(test_data.shape[0],
transformed_image_dims).astype(np.float32))
transformed_test_label = z_score(
test_label.reshape(test_label.shape[0],
transformed_image_dims).astype(np.float32))
model_epochs = 500
fit_stats = autoencoder.fit(transformed_train_data,
transformed_train_label,
batch_size=128,
epochs=model_epochs,
validation_data=(transformed_test_data,
transformed_test_label),
shuffle_data=True)
# generate non rescaled test labels for use in generated digits plot (use the same random_seed as above)
_, _, _, test_label = train_test_split(data.data,
data.target,
test_size=0.2,
random_seed=5)
predictions = autoencoder.predict(transformed_test_data).reshape(
(-1, channels, img_rows, img_cols))
model_name = autoencoder.model_name
plot_generated_img_samples(unhot(one_hot(test_label)),
predictions,
# 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)
model_name = model.model_name
plot_metric('loss',
model_epochs,
fit_stats['train_loss'],
fit_stats['valid_loss'],
model_name=model_name)
plot_metric('accuracy',
# optimizer definition
opt = register_opt(optimizer_name='adam', momentum=0.1, learning_rate=0.01)
# model definition
model = Sequential()
model.add(Dense(10, activation='sigmoid', input_shape=(train_data.shape[1], )))
model.add(Dense(3, activation='sigmoid')) # 3 iris_classes
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary('iris mlp')
model_epochs = 25
fit_stats = model.fit(train_data,
one_hot(train_label),
batch_size=10,
epochs=model_epochs,
validation_data=(test_data, one_hot(test_label)),
shuffle_data=True)
# eval_stats = model.evaluate(test_data, one_hot(train_label))
predictions = unhot(model.predict(test_data))
print_results(predictions, test_label)
model_name = model.model_name
plot_metric('loss',
model_epochs,
fit_stats['train_loss'],
fit_stats['valid_loss'],
model_name=model_name)
plot_metric('accuracy',
model_epochs,
from ztlearn.dl.models import Sequential
from ztlearn.utils import train_test_split
from ztlearn.dl.optimizers import register_opt
opt = register_opt(optimizer_name = 'sgd_momentum', momentum = 0.01, learning_rate = 0.001)
model = Sequential(init_method = 'he_normal')
model.add(Embedding(10, 2, activation = 'selu', input_shape = (1, 10)))
model.compile(loss = 'categorical_crossentropy', optimizer = opt)
train_data = np.random.randint(10, size=(5, 1, 10))
train_label = np.random.randint(14, size=(5, 1, 10))
train_data, test_data, train_label, test_label = train_test_split(train_data,
train_label,
test_size = 0.1)
fit_stats = model.fit(train_data, train_label, batch_size = 4, epochs = 50)
"""
works
data = np.arange(0,100,1).reshape(10,1,10)
labels = np.arange(1,101,1).reshape(10,1,10)
model.add(Embedding(100, 5, activation = 'selu', input_shape = (1, 10)))
model.add(RNN(10, activation="tanh", bptt_truncate = 3, input_shape = (10, 10)))
"""
