參考1
參考2

train_on_batch n_epoch = 12 batch_size = 16 for e in range(n_epoch):print("epoch", e)batch_num = 0loss_sum=np.array([0.0,0.0])for X_train, y_train in GET_DATASET_SHUFFLE(train_X, batch_size, True): # chunks of 100 images for X_batch, y_batch in train_datagen.flow(X_train, y_train, batch_size=batch_size): # chunks of 32 samplesloss = model.train_on_batch(X_batch, y_batch)loss_sum += loss batch_num += 1break #手動breakif batch_num%200==0:print("epoch %s, batch %s: train_loss = %.4f, train_acc = %.4f"%(e, batch_num, loss_sum[0]/200, loss_sum[1]/200))loss_sum=np.array([0.0,0.0])res = model.evaluate_generator(GET_DATASET_SHUFFLE(val_X, batch_size, False),int(len(val_X)/batch_size))print("val_loss = %.4f, val_acc = %.4f: "%( res[0], res[1]))model.save("weight.h5")

總結

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