N.Conv(64, (3, 3), pad='same', stride=(1, 1), activation=K.relu),
N.Pool(pool_size=(2, 2), ignore_border=True, strides=None, mode='max'),
N.Dropout(level=0.25),
N.Flatten(outdim=2),
N.Dense(512, activation=K.relu),
N.Dropout(level=0.5),
N.Dense(10, activation=K.softmax)
],
debug=True)
K.set_training(True)
y_train = f(X)
K.set_training(False)
y_pred = f(X)
cost_train = K.mean(K.categorical_crossentropy(y_train, y_true))
cost_pred = K.mean(K.categorical_accuracy(y_pred, y_true))
cost_eval = K.mean(K.categorical_crossentropy(y_pred, y_true))
parameters = f.parameters
print('Parameters:', [p.name for p in parameters])
optz = K.optimizers.RMSProp()
updates = optz.get_updates(cost_train, parameters)
print("Build training function ...")
f_train = K.function([X, y_true], cost_train, updates=updates)
print("Build scoring function ...")
f_score = K.function([X, y_true], [cost_pred, cost_eval])
# ===========================================================================
# Create trainer
# ===========================================================================
strides=1,
pad='valid',
activation=K.relu),
N.Pool(pool_size=(35, 1), pad='valid', mode='max'),
N.Flatten(outdim=2),
N.Dense(num_units=128, activation=K.relu),
N.Dense(num_units=nb_labels, activation=K.softmax)
],
debug=True)
y_pred = f(X)
params = [p for p in f.parameters if not has_roles(p, EmbeddingWeight)]
print('Params:', [p.name for p in params])
cost_train = K.mean(K.categorical_crossentropy(y_pred, y))
cost_score = K.mean(K.categorical_accuracy(y_pred, y))
opt = K.optimizers.RMSProp()
updates = opt.get_updates(cost_train, params)
print('Build training function ...')
f_train = K.function([X, y], cost_train, updates)
print('Build scoring function ...')
f_score = K.function([X, y], cost_score)
trainer = training.MainLoop(batch_size=128, seed=1208, shuffle_level=2)
trainer.set_task(f_train, (X_train, y_train),
epoch=args['epoch'],
name='train')
trainer.set_subtask(f_score, (X_valid, y_valid), freq=1., name='valid')
trainer.set_callback([
N.Conv(num_filters=128, filter_size=(5, 1), strides=1, pad='valid',
activation=K.relu),
N.Pool(pool_size=(35, 1), pad='valid', mode='max'),
N.Flatten(outdim=2),
N.Dense(num_units=128, activation=K.relu),
N.Dense(num_units=nb_labels, activation=K.softmax)
], debug=True)
y_pred = f(X)
params = [p for p in f.parameters if not has_roles(p, EmbeddingWeight)]
print('Params:', [p.name for p in params])
cost_train = K.mean(K.categorical_crossentropy(y_pred, y))
cost_score = K.mean(K.categorical_accuracy(y_pred, y))
opt = K.optimizers.RMSProp()
updates = opt.get_updates(cost_train, params)
print('Build training function ...')
f_train = K.function([X, y], cost_train, updates)
print('Build scoring function ...')
f_score = K.function([X, y], cost_score)
trainer = training.MainLoop(batch_size=128, seed=1208, shuffle_level=2)
trainer.set_task(f_train, (X_train, y_train), epoch=args['epoch'], name='train')
trainer.set_subtask(f_score, (X_valid, y_valid), freq=1., name='valid')
trainer.set_callback([
training.ProgressMonitor('train', format='Train:{:.4f}'),
training.ProgressMonitor('valid', format='Test:{:.4f}'),
N.Pool(pool_size=(2, 2), strides=None),
N.Flatten(outdim=2),
N.Dense(256, activation=K.relu),
N.Dense(10, activation=K.softmax)
],
debug=True)
ops = cPickle.loads(cPickle.dumps(ops)) # test if the ops is pickle-able
K.set_training(True)
y_pred_train = ops(X)
K.set_training(False)
y_pred_score = ops(X)
cost_train = K.mean(K.categorical_crossentropy(y_pred_train, y))
cost_test_1 = K.mean(K.categorical_crossentropy(y_pred_score, y))
cost_test_2 = K.mean(K.categorical_accuracy(y_pred_score, y))
cost_test_3 = K.confusion_matrix(y_pred_score, y, labels=range(10))
parameters = ops.parameters
optimizer = K.optimizers.SGD(lr=arg['lr'])
updates = optimizer(cost_train, parameters)
print('Building training functions ...')
f_train = K.function([X, y], [cost_train, optimizer.norm], updates=updates)
print('Building testing functions ...')
f_test = K.function([X, y], [cost_test_1, cost_test_2, cost_test_3])
print('Building predicting functions ...')
f_pred = K.function(X, y_pred_score)
# ===========================================================================
# Build trainer
# ===========================================================================
# ===========================================================================
# Build network
# ===========================================================================
ops = N.Sequence([
N.Flatten(outdim=2),
N.Dense(512, activation=K.relu),
N.Dense(256, activation=K.relu),
N.Dense(10, activation=K.softmax)
])
ops = cPickle.loads(cPickle.dumps(ops)) # test if the ops is pickle-able
y_pred_train = ops(X_train)
y_pred_score = ops(X_score)
cost_train = K.mean(K.categorical_crossentropy(y_pred_train, y))
cost_test_1 = K.mean(K.categorical_crossentropy(y_pred_score, y))
cost_test_2 = K.mean(K.categorical_accuracy(y_pred_score, y))
cost_test_3 = K.confusion_matrix(y_pred_score, y, labels=range(10))
parameters = ops.parameters
optimizer = K.optimizers.RMSProp(lr= 0.0001, clipnorm=100.)
updates = optimizer(cost_train, parameters)
print('Building training functions ...')
f_train = K.function([X_train, y], [cost_train, optimizer.norm],
updates=updates)
print('Building testing functions ...')
f_test = K.function([X_score, y], [cost_test_1, cost_test_2, cost_test_3])
# ====== normalize 0-1 ====== #
if False:
print('Normalized data in range [0-1]')
X_train = ds['X_train'][:]