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ModelCheckpoint()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 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 train_model(self, batch_size, epochs, path):
filepath = "./traindata/checkpoints/weights-{epoch:02d}.hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks_list = [checkpoint]
self.model.fit(self.x_data,
self.y_data,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks_list)
self.model.save_weights(path)
