Python callbacks.ModelCheckpoint方法代码示例

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def threadsafe_generator(f):
    """A decorator that takes a generator function and makes it thread-safe.
    """
    def g(*a, **kw):
        return ThreadsafeIterator(f(*a, **kw))
    return g

###############################################################
## MODEL CHECKPOINT FOR MULTI GPU
## When using multiple GPUs, we need to save the base model,
## not the one defined by multi_gpu_model
## see example: https://keras.io/utils/#multi_gpu_model
## Therefore, to save the model after each epoch by leveraging 
## ModelCheckpoint callback, we need to adapt it to save the 
## base model. To do so, we pass the base model to the callback.
## Inspired by: 
##   https://github.com/keras-team/keras/issues/8463#issuecomment-345914612
############################################################### 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def get_call_back():
    """
    定义call back
    :return:
    """
    checkpoint = ModelCheckpoint(filepath='/tmp/ctpn.{epoch:03d}.h5',
                                 monitor='val_loss',
                                 verbose=1,
                                 save_best_only=False,
                                 save_weights_only=True,
                                 period=5)

    # 验证误差没有提升
    lr_reducer = ReduceLROnPlateau(monitor='loss',
                                   factor=0.1,
                                   cooldown=0,
                                   patience=10,
                                   min_lr=1e-4)
    log = TensorBoard(log_dir='log')
    return [lr_reducer, checkpoint, log] 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train_model(self):

		checkpoint = ModelCheckpoint(self.PATH, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')

		if self.modality == "audio":
			model = self.get_audio_model()
			model.compile(optimizer='adadelta', loss='categorical_crossentropy', sample_weight_mode='temporal')
		elif self.modality == "text":
			model = self.get_text_model()
			model.compile(optimizer='adadelta', loss='categorical_crossentropy', sample_weight_mode='temporal')
		elif self.modality == "bimodal":
			model = self.get_bimodal_model()
			model.compile(optimizer='adam', loss='categorical_crossentropy', sample_weight_mode='temporal')

		early_stopping = EarlyStopping(monitor='val_loss', patience=10)
		model.fit(self.train_x, self.train_y,
		                epochs=self.epochs,
		                batch_size=self.batch_size,
		                sample_weight=self.train_mask,
		                shuffle=True, 
		                callbacks=[early_stopping, checkpoint],
		                validation_data=(self.val_x, self.val_y, self.val_mask))

		self.test_model() 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train_model(weight = None, batch_size=32, epochs = 10):

    cg = caption_generator.CaptionGenerator()
    model = cg.create_model()

    if weight != None:
        model.load_weights(weight)

    counter = 0
    file_name = 'weights-improvement-{epoch:02d}.hdf5'
    checkpoint = ModelCheckpoint(file_name, monitor='loss', verbose=1, save_best_only=True, mode='min')
    callbacks_list = [checkpoint]
    model.fit_generator(cg.data_generator(batch_size=batch_size), steps_per_epoch=cg.total_samples/batch_size, epochs=epochs, verbose=2, callbacks=callbacks_list)
    try:
        model.save('Models/WholeModel.h5', overwrite=True)
        model.save_weights('Models/Weights.h5',overwrite=True)
    except:
        print "Error in saving model."
    print "Training complete...\n" 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train(model, image_data, y_true, log_dir='logs/'):
    '''retrain/fine-tune the model'''
    model.compile(optimizer='adam', loss={
        # use custom yolo_loss Lambda layer.
        'yolo_loss': lambda y_true, y_pred: y_pred})

    logging = TensorBoard(log_dir=log_dir)
    checkpoint = ModelCheckpoint(log_dir + "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5",
        monitor='val_loss', save_weights_only=True, save_best_only=True)
    early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto')

    model.fit([image_data, *y_true],
              np.zeros(len(image_data)),
              validation_split=.1,
              batch_size=32,
              epochs=30,
              callbacks=[logging, checkpoint, early_stopping])
    model.save_weights(log_dir + 'trained_weights.h5')
    # Further training. 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train_simple_inference_net(n_epochs=30):
    inf_net = SimpleInferenceNet()
    tr_ids, val_ids, te_ids = train_document_ids(), validation_document_ids(), test_document_ids()
    tr_ids = list(train_document_ids())
    train_Xy, inference_vectorizer = get_train_Xy(tr_ids, sections_of_interest=None, vocabulary_file=None, include_sentence_span_splits=False, include_raw_texts=True)

    X_k, y_k = make_Xy_inference(train_Xy, inf_net.bc)
    print("train data for inference task loaded!")

    val_Xy = get_Xy(val_ids, inference_vectorizer,  include_raw_texts=True)
    X_kv, y_kv = make_Xy_inference(val_Xy, inf_net.bc)
    print("val data loaded!")

    filepath="inference.weights.best.hdf5"
    checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
    callbacks_list = [checkpoint]

    with open("inference_model.json", "w") as outf:
        outf.write(inf_net.model.to_json())

    print("fitting inference model!")
    inf_net.model.fit(X_k, y_k, validation_data=(X_kv, y_kv), callbacks=callbacks_list, epochs=n_epochs) 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train_model(self,model,X_train,X_test,y_train,y_test):
        input_y_train = self.include_start_token(y_train)
        print(input_y_train.shape)
        input_y_test = self.include_start_token(y_test)
        print(input_y_test.shape)
        early = EarlyStopping(monitor='val_loss',patience=10,mode='auto')

        checkpoint = ModelCheckpoint(self.outpath + 's2s_model_' + str(self.version) + '_.h5',monitor='val_loss',verbose=1,save_best_only=True,mode='auto')
        lr_reduce = ReduceLROnPlateau(monitor='val_loss',factor=0.5, patience=2, verbose=0, mode='auto')
        model.fit([X_train,input_y_train],y_train, 
		      epochs=self.epochs,
		      batch_size=self.batch_size, 
		      validation_data=[[X_test,input_y_test],y_test], 
		      callbacks=[early,checkpoint,lr_reduce], 
		      shuffle=True)
        return model 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train_model(model, X, X_test, Y, Y_test):
    checkpoints = []
    if not os.path.exists('Data/Checkpoints/'):
        os.makedirs('Data/Checkpoints/')
    checkpoints.append(ModelCheckpoint('Data/Checkpoints/best_weights.h5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1))
    checkpoints.append(TensorBoard(log_dir='Data/Checkpoints/./logs', histogram_freq=0, write_graph=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None))

    # Creates live data:
    # For better yield. The duration of the training is extended.

    # If you don't want, use this:
    # model.fit(X, Y, batch_size=10, epochs=25, validation_data=(X_test, Y_test), shuffle=True, callbacks=checkpoints)

    from keras.preprocessing.image import ImageDataGenerator
    generated_data = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0,  width_shift_range=0.1, height_shift_range=0.1, horizontal_flip = True, vertical_flip = False)
    generated_data.fit(X)
    import numpy
    model.fit_generator(generated_data.flow(X, Y, batch_size=8), steps_per_epoch=X.shape[0]//8, epochs=25, validation_data=(X_test, Y_test), callbacks=checkpoints)

    return model 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def get_callbacks(self,log, model_prefix='Model'):
        """
        Creates a list of callbacks that can be used during training to create a
        snapshot ensemble of the model.
        Args:
            model_prefix: prefix for the filename of the weights.
        Returns: list of 3 callbacks [ModelCheckpoint, LearningRateScheduler,
                 SnapshotModelCheckpoint] which can be provided to the 'fit' function
        """
        if not os.path.exists(self.save_dir+'/weights/'):
            os.makedirs(self.save_dir+'/weights/')

        callback_list = [callbacks.ModelCheckpoint(self.save_dir+"/weights/weights_{epoch:002d}.h5", monitor="val_capsnet_acc",
                                                    save_best_only=True, save_weights_only=False),
                         callbacks.LearningRateScheduler(schedule=self._cosine_anneal_schedule),
                         SnapshotModelCheckpoint(self.T, self.M, fn_prefix=self.save_dir+'/weights/%s' % model_prefix), log]

        return callback_list 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train():
    model = create_model()
    model.compile(optimizer='adam',
                  loss=losses.sparse_categorical_crossentropy,
                  metrics=['accuracy'])
    checkpointer = callbacks.ModelCheckpoint(filepath="../Output/checkpoint.hdf5", verbose=1, save_best_only=True)
    x_train, x_test, y_train, y_test = load_audio_data()
    model.fit(x_train,
              y_train,
              epochs=1000,
              batch_size=1000,
              validation_split=0.2,
              callbacks=[checkpointer])
    results = model.evaluate(x_test, y_test)
    print('test_results: ', results)

    model.save(MODEL_FILE_PATH) 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def NerCallbacks(id_to_tag, best_fit_params=None, mask_tag=None, log_path=None):
    """模型训练过程中的回调函数
    """
    callbacks = [Accuracy(id_to_tag, mask_tag, log_path)]
    if best_fit_params is not None:
        early_stopping = EarlyStopping(
            monitor="val_crf_accuracy",
            patience=best_fit_params.get("early_stop_patience"))
        reduce_lr_on_plateau = ReduceLROnPlateau(
            monitor="val_crf_accuracy", verbose=1, mode="max",
            factor=best_fit_params.get("reduce_lr_factor"),
            patience=best_fit_params.get("reduce_lr_patience"))
        model_check_point = ModelCheckpoint(
            best_fit_params.get("save_path"),
            monitor="val_crf_accuracy", verbose=2, mode="max", save_best_only=True)
        callbacks.extend([early_stopping, reduce_lr_on_plateau, model_check_point])
    return callbacks 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train():
    # load data
    train_dataset = Dataset(training=True)
    dev_dataset = Dataset(training=False)

    # model
    MODEL = name_model[model_name]
    model = MODEL(train_dataset.vocab_size, conf.n_classes, train_dataset.emb_mat)

    # callback
    my_callback = MyCallback()
    f1 = F1(dev_dataset.gen_batch_data(), dev_dataset.steps_per_epoch)
    checkpointer = ModelCheckpoint('data/{}.hdf5'.format(model_name), save_best_only=True)
    early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')

    # train
    model.compile(optimizer=keras.optimizers.Adam(),
                  loss=keras.losses.categorical_crossentropy, metrics=['acc'])
    model.fit_generator(train_dataset.gen_batch_data(),
                        steps_per_epoch=train_dataset.steps_per_epoch,
                        verbose=0,
                        epochs=conf.epochs, callbacks=[my_callback, checkpointer, early_stop, f1])
    keras.models.save_model(model, conf.model_path.format(model_name)) 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def cnn_model():
	num_of_classes = get_num_of_classes()
	model = Sequential()
	model.add(Conv2D(16, (2,2), input_shape=(image_x, image_y, 1), activation='relu'))
	model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))
	model.add(Conv2D(32, (3,3), activation='relu'))
	model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3), padding='same'))
	model.add(Conv2D(64, (5,5), activation='relu'))
	model.add(MaxPooling2D(pool_size=(5, 5), strides=(5, 5), padding='same'))
	model.add(Flatten())
	model.add(Dense(128, activation='relu'))
	model.add(Dropout(0.2))
	model.add(Dense(num_of_classes, activation='softmax'))
	sgd = optimizers.SGD(lr=1e-2)
	model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
	filepath="cnn_model_keras2.h5"
	checkpoint1 = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
	callbacks_list = [checkpoint1]
	#from keras.utils import plot_model
	#plot_model(model, to_file='model.png', show_shapes=True)
	return model, callbacks_list 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def train(model, max_len=200000, batch_size=64, verbose=True, epochs=100, save_path='../saved/', save_best=True):
    
    # callbacks
    ear = EarlyStopping(monitor='val_acc', patience=5)
    mcp = ModelCheckpoint(join(save_path, 'malconv.h5'), 
                          monitor="val_acc", 
                          save_best_only=save_best, 
                          save_weights_only=False)
    
    history = model.fit_generator(
        utils.data_generator(x_train, y_train, max_len, batch_size, shuffle=True),
        steps_per_epoch=len(x_train)//batch_size + 1,
        epochs=epochs, 
        verbose=verbose, 
        callbacks=[ear, mcp],
        validation_data=utils.data_generator(x_test, y_test, max_len, batch_size),
        validation_steps=len(x_test)//batch_size + 1)
    return history 

# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import ModelCheckpoint [as 别名]
def finetuning_callbacks(checkpoint_path, patience, verbose):
    """ Callbacks for model training.

    # Arguments:
        checkpoint_path: Where weight checkpoints should be saved.
        patience: Number of epochs with no improvement after which
            training will be stopped.

    # Returns:
        Array with training callbacks that can be passed straight into
        model.fit() or similar.
    """
    cb_verbose = (verbose >= 2)
    checkpointer = ModelCheckpoint(monitor='val_loss', filepath=checkpoint_path,
                                   save_best_only=True, verbose=cb_verbose)
    earlystop = EarlyStopping(monitor='val_loss', patience=patience,
                              verbose=cb_verbose)
    return [checkpointer, earlystop]