[neural netrowks]How do i resume training once i have saved the model

[Prince_Bhatia]

I have a model that I've trained for 75 epochs. saved the model with model.save(). The code for training is

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from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential, load_model from keras.layers import Conv2D, MaxPooling2D from keras.layers import Activation, Dropout, Flatten, Dense from keras import backend as K     # dimensions of our images. img_width, img_height = 320, 240   train_data_dir = 'dataset/Training_set' validation_data_dir = 'dataset/Test_set' nb_train_samples = 4000  #total nb_validation_samples = 1000  # total epochs = 25 batch_size = 10   if K.image_data_format() == 'channels_first':     input_shape = (3, img_width, img_height) else:     input_shape = (img_width, img_height, 3)   model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))   model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))   model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))   model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid'))   model.compile(loss='binary_crossentropy',               optimizer='rmsprop',               metrics=['accuracy'])   # this is the augmentation configuration we will use for training train_datagen = ImageDataGenerator(     rescale=1. / 255,     shear_range=0.2,     zoom_range=0.2,     horizontal_flip=True)   # this is the augmentation configuration we will use for testing: # only rescaling test_datagen = ImageDataGenerator(rescale=1. / 255)   train_generator = train_datagen.flow_from_directory(     train_data_dir,     target_size=(img_width, img_height),     batch_size=batch_size,     class_mode='binary')   validation_generator = test_datagen.flow_from_directory(     validation_data_dir,     target_size=(img_width, img_height),     batch_size=batch_size,     class_mode='binary')   model.fit_generator(     train_generator,     steps_per_epoch=nb_train_samples // batch_size,     epochs=epochs,     validation_data=validation_generator,     validation_steps=5)   model.save('model1.h5')

How do i restart training ? just run this code again ? or do i need to make some changes ? and what are those changes?

[schuler]

You'll need to load the model with model.load('model1.h5') and then call fit_generator again.