训练模型CNN KERAS

Question

大家好，我正在尝试使用CNN和keras训练一个模型，但是训练没有完成，我得到了这个警告，它停止了训练，我不知道为什么，我不明白问题在哪里，谁能给我一个建议或者我应该在代码中改变什么

def myModel():
no_Of_Filters=60
size_of_Filter=(5,5) # THIS IS THE KERNEL THAT MOVE AROUND THE IMAGE TO GET THE FEATURES.
# THIS WOULD REMOVE 2 PIXELS FROM EACH BORDER WHEN USING 32 32 IMAGE
size_of_Filter2=(3,3)
size_of_pool=(2,2)  # SCALE DOWN ALL FEATURE MAP TO GERNALIZE MORE, TO REDUCE OVERFITTING
no_Of_Nodes = 500   # NO. OF NODES IN HIDDEN LAYERS
model= Sequential()
model.add((Conv2D(no_Of_Filters,size_of_Filter,input_shape=(imageDimesions[0],imageDimesions[1],1),activation='relu')))  # ADDING MORE CONVOLUTION LAYERS = LESS FEATURES BUT CAN CAUSE ACCURACY TO INCREASE
model.add((Conv2D(no_Of_Filters, size_of_Filter, activation='relu')))
model.add(MaxPooling2D(pool_size=size_of_pool)) # DOES NOT EFFECT THE DEPTH/NO OF FILTERS

model.add((Conv2D(no_Of_Filters//2, size_of_Filter2,activation='relu')))
model.add((Conv2D(no_Of_Filters // 2, size_of_Filter2, activation='relu')))
model.add(MaxPooling2D(pool_size=size_of_pool))
model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(no_Of_Nodes,activation='relu'))
model.add(Dropout(0.5)) # INPUTS NODES TO DROP WITH EACH UPDATE 1 ALL 0 NONE
model.add(Dense(noOfClasses,activation='softmax')) # OUTPUT LAYER
# COMPILE MODEL
model.compile(Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])
return model


############################### TRAIN
model = myModel()
print(model.summary())
history=model.fit_generator(dataGen.flow(X_train,y_train,batch_size=batch_size_val),steps_per_epoch=steps_per_epoch_val,epochs=epochs_val,validation_data=(X_validation,y_validation),shuffle=1)
WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 20000 batches). You may need to use the repeat() function when building your dataset.

Answer 1

在使用生成器时，您可以在不使用step_per_epoch参数的情况下运行模型，并让模型计算出覆盖一个epoch需要多少步。

history=model.fit_generator(dataGen.flow(X_train,y_train,batch_size=batch_size_val),
epochs=epochs_val,
validation_data=(X_validation,y_validation),
shuffle=1)

或

你必须计算steps_per_epoch并在训练时使用它，如下所示;

history=model.fit_generator(dataGen.flow(X_train,y_train,batch_size=batch_size_val),
steps_per_epoch=(data_samples/batch_size)
epochs=epochs_val,
validation_data=(X_validation,y_validation),
shuffle=1)

如果问题仍然存在，请告诉我们。谢谢!