本文整理汇总了Python中keras.callbacks.LambdaCallback方法的典型用法代码示例。如果您正苦于以下问题:Python callbacks.LambdaCallback方法的具体用法?Python callbacks.LambdaCallback怎么用?Python callbacks.LambdaCallback使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.callbacks
的用法示例。
在下文中一共展示了callbacks.LambdaCallback方法的14个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_callbacks
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def get_callbacks(self, X):
"""
Sets these callbacks as a class member.
X is the encoded dataset used to print a sample of the output.
Callbacks created:
1. Sample output each epoch
2. Save best performing model each epoch
"""
sample_output_callback = LambdaCallback(on_epoch_end = lambda epoch, logs:\
logging.debug(pformat(self.sample_labels(self.model.predict(X)))))
checkpoint = ModelCheckpoint(os.path.join(self.model_dir,
"weights.hdf5"),
verbose = 1,
save_best_only = False) # TODO: is there a way to save by best val_acc?
return [sample_output_callback,
checkpoint]
示例2: get_callbacks
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def get_callbacks(self, X):
"""
Returns callbacks listed below.
X is the encoded dataset used to print a sample of the output.
Callbacks created:
1. Sample output each epoch
2. Save best performing model each epoch
"""
sample_output_callback = LambdaCallback(on_epoch_end = lambda epoch, logs:\
pprint(self.sample_labels(self.model.predict(X))))
checkpoint = ModelCheckpoint(os.path.join(self.model_dir,
"weights.hdf5"),
verbose = 1,
save_best_only = False) # TODO: is there a way to save by best val_acc?
return [sample_output_callback,
checkpoint]
示例3: find
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def find(self, x_train, y_train, start_lr, end_lr, batch_size=64, epochs=1, **kw_fit):
# If x_train contains data for multiple inputs, use length of the first input.
# Assumption: the first element in the list is single input; NOT a list of inputs.
N = x_train[0].shape[0] if isinstance(x_train, list) else x_train.shape[0]
# Compute number of batches and LR multiplier
num_batches = epochs * N / batch_size
self.lr_mult = (float(end_lr) / float(start_lr)) ** (float(1) / float(num_batches))
# Save weights into a file
initial_weights = self.model.get_weights()
# Remember the original learning rate
original_lr = K.get_value(self.model.optimizer.lr)
# Set the initial learning rate
K.set_value(self.model.optimizer.lr, start_lr)
callback = LambdaCallback(on_batch_end=lambda batch, logs: self.on_batch_end(batch, logs))
self.model.fit(x_train, y_train,
batch_size=batch_size, epochs=epochs,
callbacks=[callback],
**kw_fit)
# Restore the weights to the state before model fitting
self.model.set_weights(initial_weights)
# Restore the original learning rate
K.set_value(self.model.optimizer.lr, original_lr)
示例4: find_generator
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def find_generator(self, generator, start_lr, end_lr, epochs=1, steps_per_epoch=None, **kw_fit):
if steps_per_epoch is None:
try:
steps_per_epoch = len(generator)
except (ValueError, NotImplementedError) as e:
raise e('`steps_per_epoch=None` is only valid for a'
' generator based on the '
'`keras.utils.Sequence`'
' class. Please specify `steps_per_epoch` '
'or use the `keras.utils.Sequence` class.')
self.lr_mult = (float(end_lr) / float(start_lr)) ** (float(1) / float(epochs * steps_per_epoch))
# Save weights into a file
initial_weights = self.model.get_weights()
# Remember the original learning rate
original_lr = K.get_value(self.model.optimizer.lr)
# Set the initial learning rate
K.set_value(self.model.optimizer.lr, start_lr)
callback = LambdaCallback(on_batch_end=lambda batch,
logs: self.on_batch_end(batch, logs))
self.model.fit_generator(generator=generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
callbacks=[callback],
**kw_fit)
# Restore the weights to the state before model fitting
self.model.set_weights(initial_weights)
# Restore the original learning rate
K.set_value(self.model.optimizer.lr, original_lr)
示例5: test_LambdaCallback
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def test_LambdaCallback():
np.random.seed(1337)
(X_train, y_train), (X_test, y_test) = get_test_data(num_train=train_samples,
num_test=test_samples,
input_shape=(input_dim,),
classification=True,
num_classes=num_classes)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
model = Sequential()
model.add(Dense(num_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# Start an arbitrary process that should run during model training and be terminated after training has completed.
def f():
while True:
pass
p = multiprocessing.Process(target=f)
p.start()
cleanup_callback = callbacks.LambdaCallback(on_train_end=lambda logs: p.terminate())
cbks = [cleanup_callback]
model.fit(X_train, y_train, batch_size=batch_size,
validation_data=(X_test, y_test), callbacks=cbks, epochs=5)
p.join()
assert not p.is_alive()
示例6: image_saver_callback
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def image_saver_callback(model, directory, epoch_interval=1, batch_interval=100, cmap='gray', render_videos=False):
def save_image(weights, batch, layer_name, i):
global current_epoch
weight = str(i + 1).zfill(2)
epoch = str(current_epoch).zfill(3)
fold = os.path.join(directory, 'epoch_{}-layer_{}-weights_{}'.format(epoch, layer_name, weight))
if not os.path.isdir(fold):
os.makedirs(fold)
name = os.path.join('{}'.format(fold),
'{}_{}x{}.png'.format(str(batch).zfill(9),
weights.shape[0], weights.shape[1]))
plt.imsave(name, weights, cmap=cmap)
def save_weight_images(batch, logs):
global current_epoch
if current_epoch % epoch_interval == 0 and batch % batch_interval == 0:
for layer in model.layers:
if len(layer.get_weights()) > 0:
for i, weights in enumerate(layer.get_weights()):
if len(weights.shape) < 2:
weights = np.expand_dims(weights, axis=0)
save_image(weights, batch, layer.name, i)
def on_epoch_begin(epoch, logs):
global current_epoch
current_epoch = epoch
def on_train_end(logs):
src = os.path.dirname(os.path.abspath(__file__))
cmd = os.path.join(src, '..', 'bin', 'create_image_sequence.sh')
print(os.system('{} {}'.format(cmd, directory)))
kwargs = dict()
kwargs['on_batch_begin'] = save_weight_images
kwargs['on_epoch_begin'] = on_epoch_begin
if render_videos:
kwargs['on_train_end'] = on_train_end
return LambdaCallback(**kwargs)
示例7: _get_callbacks
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def _get_callbacks(self):
"""
Returns a set of Callbacks which are used to perform various functions within Keras' .fit method.
Here, we use an early stopping callback to add patience with respect to the validation metric and
a Lambda callback which performs the model specific callbacks which you might want to build into
a model, such as re-encoding some background knowledge.
Additionally, there is also functionality to create Tensorboard log files. These can be visualised
using 'tensorboard --logdir /path/to/log/files' after training.
"""
early_stop = EarlyStopping(monitor=self.validation_metric, patience=self.patience)
model_callbacks = LambdaCallback(on_epoch_begin=lambda epoch, logs: self._pre_epoch_hook(epoch),
on_epoch_end=lambda epoch, logs: self._post_epoch_hook(epoch))
callbacks = [early_stop, model_callbacks]
if self.debug_params:
debug_callback = LambdaCallback(on_epoch_end=lambda epoch, logs:
self.__debug(self.debug_params["layer_names"],
self.debug_params.get("masks", []), epoch))
callbacks.append(debug_callback)
return CallbackList(callbacks)
# Some witchcraft is happening here - we don't specify the epoch replacement variable
# checkpointing string, because Keras does that within the callback if we specify it here.
if self.save_models:
checkpointing = ModelCheckpoint(self.model_prefix + "_weights_epoch={epoch:d}.h5",
save_best_only=True, save_weights_only=True,
monitor=self.validation_metric)
callbacks.append(checkpointing)
return CallbackList(callbacks)
示例8: experiment
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def experiment(opt, path):
"""Train two players to play rock, paper, scissors using a given optimizer"""
x = Input((1,), name="x")
player_a = Dense(3, activation='softmax', name="player_a", bias=False, W_regularizer=l2(1e-2))
player_b = Dense(3, activation='softmax', name="player_b", bias=False, W_regularizer=l2(1e-2))
action_a = player_a(x)
action_b = player_b(x)
def rps(z):
u = z[0]
v = z[1]
return u[:, 0] * v[:, 2] + u[:, 1] * v[:, 0] + u[:, 2] * v[:, 1]
model_a = Model(x, merge([action_a, action_b], mode=rps, output_shape=lambda z: (z[0][0], 1)))
model_b = Model(x, merge([action_b, action_a], mode=rps, output_shape=lambda z: (z[0][0], 1)))
adversarial_model = AdversarialModel(player_models=[model_a, model_b],
player_params=[[player_a.W], [player_b.W]],
player_names=["a", "b"])
adversarial_model.adversarial_compile(opt,
player_optimizers=[SGD(1), SGD(1)],
loss="mean_absolute_error")
param_model = Model(x, [action_a, action_b])
def print_params(epoch, logs):
params = param_model.predict(np.ones((1, 1)))
a = params[0].ravel()
b = params[1].ravel()
print("Epoch: {}, A: {}, B: {}".format(epoch, a, b))
imgpath = os.path.join(path, "epoch-{:03d}.png".format(epoch))
rps_chart(imgpath, a, b)
cb = LambdaCallback(on_epoch_begin=print_params)
batch_count = 5
adversarial_model.fit(np.ones((batch_count, 1)),
[np.ones((batch_count, 1)), np.ones((batch_count, 1))],
nb_epoch=120, callbacks=[cb], verbose=0, batch_size=1)
示例9: train_model
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def train_model(self):
""" train the model """
callbacks = []
callbacks.append(TensorBoard(self.graph_path))
callbacks.append(LearningRateScheduler(lambda e: self.learning_rate * 0.999 ** (e / 20)))
callbacks.append(ModelCheckpoint(self.checkpoint_path + 'checkpoint.best.hdf5', save_best_only=True))
if not self.best_cp:
callbacks.append(ModelCheckpoint(self.checkpoint_path + 'checkpoint.{epoch:02d}-{val_loss:.2f}.hdf5'))
callbacks.append(LambdaCallback(on_epoch_end=lambda epoch, logs: self.save_image('test.{e:02d}-{val_loss:.2f}'.format(e=epoch, **logs))))
self.model.compile(Adam(lr=self.learning_rate), binary_crossentropy)
self.model.fit(self.corrupted['train'], self.source['train'],
batch_size=self.batch_size,
epochs=self.epoch,
callbacks=callbacks,
validation_data=(self.corrupted['valid'], self.source['valid']))
示例10: train_all
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def train_all(self, epochs=1000, lr=1e-3):
checkdir = "checkpoint"
try:
os.mkdir(checkdir)
except FileExistsError:
print("Folder exists: ", checkdir)
filename = self.settings.dataset
filename += ".densemapnet.weights.{epoch:02d}.h5"
filepath = os.path.join(checkdir, filename)
checkpoint = ModelCheckpoint(filepath=filepath,
save_weights_only=True,
verbose=1,
save_best_only=False)
predict_callback = LambdaCallback(on_epoch_end=lambda epoch,
logs: self.predict_disparity())
callbacks = [checkpoint, predict_callback]
self.load_train_data(1)
if self.network is None:
self.network = DenseMapNet(settings=self.settings)
self.model = self.network.build_model(lr=lr)
if self.settings.otanh:
print("Using loss=mse on tanh output layer")
self.model.compile(loss='mse',
optimizer=RMSprop(lr=lr, decay=1e-6))
else:
print("Using loss=crossent on sigmoid output layer")
self.model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=lr, decay=1e-6))
if self.settings.model_weights:
if self.settings.notrain:
self.predict_disparity()
return
x = [self.train_lx, self.train_rx]
self.model.fit(x,
self.train_dx,
epochs=epochs,
batch_size=4,
shuffle=True,
callbacks=callbacks)
示例11: test_CallbackValData
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def test_CallbackValData():
np.random.seed(1337)
(X_train, y_train), (X_test, y_test) = get_test_data(num_train=train_samples,
num_test=test_samples,
input_shape=(input_dim,),
classification=True,
num_classes=num_classes)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
model = Sequential()
model.add(Dense(num_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
cbk = callbacks.LambdaCallback(on_train_end=lambda x: 1)
model.fit(X_train, y_train, batch_size=batch_size,
validation_data=(X_test, y_test), callbacks=[cbk], epochs=1)
def data_generator(train):
if train:
max_batch_index = len(X_train) // batch_size
else:
max_batch_index = len(X_test) // batch_size
i = 0
while 1:
if train:
yield (X_train[i * batch_size: (i + 1) * batch_size],
y_train[i * batch_size: (i + 1) * batch_size])
else:
yield (X_test[i * batch_size: (i + 1) * batch_size],
y_test[i * batch_size: (i + 1) * batch_size])
i += 1
i = i % max_batch_index
cbk2 = callbacks.LambdaCallback(on_train_end=lambda x: 1)
model.fit_generator(data_generator(True), len(X_train), epochs=1,
validation_data=(X_test, y_test),
callbacks=[cbk2])
# callback validation data should always have x, y, and sample weights
assert len(cbk.validation_data) == len(cbk2.validation_data) == 3
assert cbk.validation_data[0] is cbk2.validation_data[0]
assert cbk.validation_data[1] is cbk2.validation_data[1]
assert cbk.validation_data[2].shape == cbk2.validation_data[2].shape
示例12: main
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def main():
model = myVGG.VGG_16()
if args.test is not None:
print ("Test mode")
img = cv2.imread(args.test)
img = fu.preprocessing(img)
img = np.expand_dims(img, axis=0)
y = np.expand_dims(np.asarray([0]), axis=0)
batch_size = 1
model.fit(img, y, nb_epoch=400, \
batch_size=batch_size, \
validation_split=0.2, \
shuffle=True, verbose=0)
return
#input_path = args.data_path
#print("training data path : " + input_path)
#X_train, y_train = fu.extract_features(input_path)
X_fname = '../data/X_train_train.npy'
y_fname = '../data/y_train_train.npy'
X_train = np.load(X_fname)
y_train = np.load(y_fname)
print(X_train.shape)
print(y_train.shape)
print("Training started")
callbacks = []
earlystop_callback = EarlyStopping(monitor='val_loss', patience=5, verbose=0)
batch_print_callback = LambdaCallback(on_batch_begin=lambda batch, logs: print(batch))
epoch_print_callback = LambdaCallback(on_epoch_end=lambda epoch, logs: print("epoch:", epoch))
callbacks.append(earlystop_callback)
callbacks.append(batch_print_callback)
callbacks.append(epoch_print_callback)
batch_size = 512
model.fit(X_train, y_train, nb_epoch=400, \
batch_size=batch_size, \
validation_split=0.2, \
shuffle=True, verbose=0, \
callbacks=callbacks)
model.save_weights('my_model_weights.h5')
scores = model.evaluate(X_train, y_train, verbose=0)
print ("Train loss : %.3f" % scores[0])
print ("Train accuracy : %.3f" % scores[1])
print ("Training finished")
开发者ID:a514514772,项目名称:Real-Time-Facial-Expression-Recognition-with-DeepLearning,代码行数:50,代码来源:model_training.py
示例13: train_all
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def train_all(self, epochs=1000, lr=1e-3):
checkdir = "checkpoint"
try:
os.mkdir(checkdir)
except FileExistsError:
print("Folder exists: ", checkdir)
filename = self.settings.dataset
filename += ".densemapnet.weights.{epoch:02d}.h5"
filepath = os.path.join(checkdir, filename)
checkpoint = ModelCheckpoint(filepath=filepath,
save_weights_only=True,
verbose=1,
save_best_only=False)
predict_callback = LambdaCallback(on_epoch_end=lambda epoch,
logs: self.predict_disparity())
callbacks = [checkpoint, predict_callback]
self.load_train_data(1)
if self.network is None:
self.network = DenseMapNet(settings=self.settings)
self.model = self.network.build_model()
# print("Using loss=mse_disparity on final conv layer and Adam")
print("Using loss=mae_disparity on final conv layer and Adam")
print(self.settings.lr)
# self.model.compile(loss='mse',
# self.model.compile(loss=_loss_mse_disparity,
self.model.compile(loss=_loss_mae_disparity,
# self.model.compile(loss=_loss_3pe_disparity,
# optimizer=RMSprop(lr=lr, decay=1e-6))
optimizer=Adam(lr=self.settings.lr))
if self.settings.model_weights:
if self.settings.notrain:
self.predict_disparity()
return
x = [self.train_lx, self.train_rx]
self.model.fit(x,
self.train_dx,
epochs=epochs,
batch_size=self.settings.batch_size,
shuffle=True,
callbacks=callbacks)
示例14: train_model
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import LambdaCallback [as 别名]
def train_model(self):
# setup checkpoint folder
checkdir = "checkpoint"
try:
os.mkdir(checkdir)
except FileExistsError:
print("Folder exists: ", checkdir)
# loop on epochs
lr = self.args.lr + self.args.decay
for i in range(1, self.args.n_epochs + 1):
# update the learning rate
lr = lr - self.args.decay
# randomize order of train files
indexes = np.arange(1, self.args.n_trains + 1)
np.random.shuffle(indexes)
# loop on train files
is_compiled = False
for j in indexes:
# load training file
self.load_train_data(j)
# setup callback for writing new model checkpoint files
filename = "us3d.icnet.weights.%d-%d.h5" % (i, j)
filepath = os.path.join(checkdir, filename)
checkpoint = ModelCheckpoint(filepath=filepath, save_weights_only=True, verbose=1, save_best_only=False)
predict_callback = LambdaCallback(on_epoch_end=lambda epoch, logs: self.compute_accuracy())
callbacks = [checkpoint, predict_callback]
# build the model, compile, and fit
height = self.train_images[0].shape[0]
width = self.train_images[0].shape[1]
bands = self.train_images[0].shape[2]
myloss = tversky_loss
if self.model is None:
self.model = build_icnet(height, width, bands, self.n_classes, weights_path=self.args.checkpoint,
train=True)
if not is_compiled:
self.model.compile(optimizer=Adam(lr=lr), loss=myloss, loss_weights=[1.0, 0.4, 0.16])
is_compiled = True
self.model.fit(self.train_images, [self.Y1, self.Y2, self.Y3], epochs=1,
batch_size=self.args.batch_size, shuffle=True, callbacks=callbacks)
# convert category value image to RGB color image