def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
python类EarlyStopping()的实例源码
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=1
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def build_keras_fit_callbacks(model_path):
from keras import callbacks
return [
callbacks.EarlyStopping(
monitor='val_loss',
patience=20
#verbose=1
),
callbacks.ModelCheckpoint(
model_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=0
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
min_lr=1e-7,
factor=0.2,
verbose=0
)
]
def fit(self, X_train, y_train, X_val, y_val, nb_classes=None, batch_size=10, nb_epoch=20, verbose=0):
model = self.model
if nb_classes is None:
nb_classes = max(set(y_train)) + 1
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_val = np_utils.to_categorical(y_val, nb_classes)
model.reset_states()
earlyStopping = callbacks.EarlyStopping(
monitor='val_loss', patience=3, verbose=verbose, mode='auto')
X_train, X_val = self.X_reshape(X_train, X_val)
history = model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=verbose, validation_data=(X_val, Y_val), callbacks=[earlyStopping])
self.nb_classes = nb_classes
self.history = history
def fit(self, X_train, Y_train, X_val, Y_val, batch_size=10, nb_epoch=20, verbose=0):
model = self.model
# if nb_classes is None:
# nb_classes = max( set( y_train)) + 1
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_val = np_utils.to_categorical(y_val, nb_classes)
model.reset_states()
earlyStopping = callbacks.EarlyStopping(
monitor='val_loss', patience=3, verbose=verbose, mode='auto')
X_train, X_val = self.X_reshape(X_train, X_val)
history = model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=verbose, validation_data=(X_val, Y_val), callbacks=[earlyStopping])
#self.nb_classes = nb_classes
self.history = history
def fit(self, X_train, y_train, X_val, y_val, nb_classes=None, batch_size=10, nb_epoch=20, verbose=0):
model = self.model
if nb_classes is None:
nb_classes = max(set(y_train)) + 1
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_val = np_utils.to_categorical(y_val, nb_classes)
model.reset_states()
earlyStopping = callbacks.EarlyStopping(
monitor='val_loss', patience=3, verbose=verbose, mode='auto')
X_train, X_val = self.X_reshape(X_train, X_val)
history = model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=verbose, validation_data=(X_val, Y_val), callbacks=[earlyStopping])
self.nb_classes = nb_classes
self.history = history
def fit(self, X_train, Y_train, X_val, Y_val, batch_size=10, nb_epoch=20, verbose=0):
model = self.model
# if nb_classes is None:
# nb_classes = max( set( y_train)) + 1
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_val = np_utils.to_categorical(y_val, nb_classes)
model.reset_states()
earlyStopping = callbacks.EarlyStopping(
monitor='val_loss', patience=3, verbose=verbose, mode='auto')
X_train, X_val = self.X_reshape(X_train, X_val)
history = model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=verbose, validation_data=(X_val, Y_val), callbacks=[earlyStopping])
#self.nb_classes = nb_classes
self.history = history
def fit( self, X_train, Y_train, X_val, Y_val, batch_size=10, nb_epoch=20, verbose = 0):
model = self.model
#if nb_classes is None:
# nb_classes = max( set( y_train)) + 1
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_val = np_utils.to_categorical(y_val, nb_classes)
model.reset_states()
earlyStopping=callbacks.EarlyStopping(monitor='val_loss', patience=3, verbose=verbose, mode='auto')
X_train, X_val = self.X_reshape( X_train, X_val)
history = model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=verbose, validation_data=(X_val, Y_val), callbacks=[earlyStopping])
#self.nb_classes = nb_classes
self.history = history
