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'])
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,
dataset='cifar',
channels=3,
to_save=False,
iteration=model_epochs,
model_name=model_name)
plot_metric('loss',
model_epochs,
fit_stats['train_loss'],
fit_stats['valid_loss'],
model_name=model_name)
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(100))
model.add(Activation('softmax'))
model.compile(loss = 'cce', optimizer = opt)
model.summary(model_name = 'cifar-100 mlp')
model_epochs = 12 # change to 200 epochs
fit_stats = model.fit(reshaped_train_data,
one_hot(train_label),
batch_size = 128,
epochs = model_epochs,
validation_data = (reshaped_test_data, one_hot(test_label)),
shuffle_data = True)
eval_stats = model.evaluate(reshaped_test_data, one_hot(test_label))
predictions = unhot(model.predict(reshaped_test_data, True))
print_results(predictions, test_label)
plot_img_results(test_data, test_label, predictions, dataset = 'cifar', channels = 3)
plot_metric('loss', model_epochs, fit_stats['train_loss'], fit_stats['valid_loss'], model_name = model.model_name)
plot_metric('accuracy', model_epochs, fit_stats['train_acc'], fit_stats['valid_acc'], model_name = model.model_name)
plot_metric('evaluation',
eval_stats['valid_batches'],
eval_stats['valid_loss'],
eval_stats['valid_acc'],
model_name = model_name,
legend = ['loss', 'acc'])
model.add(BatchNormalization())
model.add(Dense(10, activation='softmax')) # 10 digits classes
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary('cifar-10 cnn')
model_epochs = 12 # change to 12 epochs
fit_stats = model.fit(train_data.reshape(-1, 3, 32, 32),
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(test_data.reshape(-1, 3, 32, 32),
one_hot(test_label)),
shuffle_data=True)
predictions = unhot(model.predict(test_data.reshape(-1, 3, 32, 32), True))
print_results(predictions, test_label)
plot_img_results(test_data,
test_label,
predictions,
dataset='cifar',
channels=3)
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,
model.add(Dense(10, activation='relu')) # 10 digits classes
model.compile(loss='cce', optimizer=opt)
model.summary()
model_epochs = 5
fit_stats = model.fit(train_data,
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(test_data, one_hot(test_label)),
shuffle_data=True)
eval_stats = model.evaluate(test_data, one_hot(test_label))
predictions = unhot(model.predict(test_data, True))
print_results(predictions, test_label)
plot_img_results(test_data[:40], test_label[:40], predictions,
dataset='mnist') # truncate to 40 samples
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)
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'])
# 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',
model_epochs,
fit_stats['train_acc'],
fit_stats['valid_acc'],
model_name=model_name)
autoencoder = Sequential(init_method=init_type)
autoencoder.layers.extend(encoder.layers)
autoencoder.layers.extend(decoder.layers)
autoencoder.compile(loss='categorical_crossentropy', optimizer=opt)
images = range_normalize(data.data.astype(np.float32), 0,
1) # rescale to range [0, 1]
train_data, test_data, train_label, test_label = train_test_split(
images, images, test_size=0.2, random_seed=15)
model_epochs = 500
fit_stats = autoencoder.fit(train_data,
train_label,
batch_size=64,
epochs=model_epochs,
validation_data=(test_data, test_label),
shuffle_data=True)
# generate non rescaled test labels for use in generated digits plot
_, _, _, test_label = train_test_split(data.data,
data.target,
test_size=0.2,
random_seed=15)
predictions = autoencoder.predict(test_data).reshape((-1, img_rows, img_cols))
plot_generated_digits_samples(unhot(one_hot(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'])
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,
fit_stats['train_acc'],
fit_stats['valid_acc'],
model_name=model_name)
model.add(Activation('softmax'))
model.compile(loss='cce', optimizer=opt)
model.summary(model_name='cifar-10 mlp')
model_epochs = 200 # change to 200 epochs
fit_stats = model.fit(transformed_train_data,
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(transformed_test_data,
one_hot(test_label)),
shuffle_data=True)
eval_stats = model.evaluate(transformed_test_data, one_hot(test_label))
predictions = unhot(model.predict(transformed_test_data, True))
print_results(predictions, test_label)
plot_img_results(test_data,
test_label,
predictions,
dataset='cifar',
channels=3)
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',
opt = register_opt(optimizer_name = 'adamax', momentum = 0.01, lr = 0.001)
model = Sequential()
model.add(Embedding(vocab_size, 2, input_length = longest_sentence))
model.add(RNN(5, activation = 'tanh', bptt_truncate = 2, input_shape = (2, longest_sentence)))
model.add(Flatten())
model.add(Dense(2, activation = 'softmax'))
model.compile(loss = 'bce', optimizer = opt)
model.summary('embedded sentences rnn')
"""
NOTE:
batch size should be equal the size of embedding
vectors and divisible by the training set size
"""
model_epochs = 500
fit_stats = model.fit(train_data,
train_label,
batch_size = 2,
epochs = model_epochs,
validation_data = (test_data, test_label))
plot_metric('loss', model_epochs, fit_stats['train_loss'], fit_stats['valid_loss'], model_name = model.model_name)
plot_metric('accuracy', model_epochs, fit_stats['train_acc'], fit_stats['valid_acc'], model_name = model.model_name)
# test out with the first sentence - sentences_tokens[0]
output_array = model.predict(np.expand_dims(sentences_tokens[0], axis=0))
print(np.argmax(output_array))
# model definition
model = Sequential()
model.add(RNN(5, activation='tanh', bptt_truncate=5, input_shape=(9, seq_len)))
model.add(Flatten())
model.add(Dense(seq_len, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary('seq rnn')
model_epochs = 15
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), test_label, test_data)
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)
reshaped_train_data = z_score(train_data.reshape(train_data.shape[0], reshaped_image_dims).astype('float32'))
reshaped_test_data = z_score(test_data.reshape(test_data.shape[0], reshaped_image_dims).astype('float32'))
# optimizer definition
opt = register_opt(optimizer_name = 'adam', momentum = 0.01, lr = 0.0001)
# model definition
model = Sequential()
model.add(RNN(256, activation = 'tanh', bptt_truncate = 5, input_shape = (3, 1024)))
model.add(Flatten())
model.add(Dense(10, activation = 'softmax')) # 10 digits classes
model.compile(loss = 'categorical_crossentropy', optimizer = opt)
model.summary(model_name = 'cifar-10 rnn')
model_epochs = 100 # add more epochs
fit_stats = model.fit(reshaped_train_data.reshape(-1, 3, 1024),
one_hot(train_label),
batch_size = 128,
epochs = model_epochs,
validation_data = (reshaped_test_data.reshape(-1, 3, 1024), one_hot(test_label)),
shuffle_data = True)
predictions = unhot(model.predict(reshaped_test_data.reshape(-1, 3, 1024), True))
print_results(predictions, test_label)
plot_img_results(test_data, test_label, predictions, dataset = 'cifar', channels = 3)
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(Dense(10, activation='softmax')) # 10 digits classes
model.compile(loss='categorical_crossentropy', optimizer=opt)
model.summary(model_name='cifar-10 rnn')
model_epochs = 100 # add more epochs
fit_stats = model.fit(reshaped_train_data.reshape(-1, 3, 1024),
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(reshaped_test_data.reshape(-1, 3, 1024),
one_hot(test_label)),
shuffle_data=True)
predictions = unhot(
model.predict(reshaped_test_data.reshape(-1, 3, 1024), True))
print_results(predictions, test_label)
plot_img_results(test_data,
test_label,
predictions,
dataset='cifar',
channels=3)
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,
