In a CNN neural network model i am trying to fit my data to fit.model () but it's showing error-CodePudding

Here,

X_train = 75% of my cancer image data, which has 3 classes.
Y_train = images are labeled as [0,0,0,0,0,1,1,1,1,1,1,1,2,2,2,2,2] 
X_test = 25% image of my cancer dataset 

results = model.fit(X_train,Y_train, X_test, validation_split=0.1, batch_size=6, epochs=5,

##################################

But getting this errors, which should I pass into model.fit()

TypeError                                 Traceback (most recent call last)
<ipython-input-132-c29910126b61> in <module>()
  5         tf.keras.callbacks.TensorBoard(log_dir='logs')]
  6 
----> 7 results = model.fit(X_train,Y_train, X_test, validation_split=0.1, batch_size=6, 
epochs=5, callbacks=callbacks)
  8 
  9 ####################################

1 frames
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     62     filtered_tb = None
     63     try:
---> 64       return fn(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)

TypeError: fit() got multiple values for argument 'batch_size'

CodePudding user response：

The fit method doesn't know what to do with X_test, the validation data needs to be passed explicitly

history = model.fit(
    x_train,
    y_train,
    batch_size=64,
    epochs=2,
    # We pass some validation for
    # monitoring validation loss and metrics
    # at the end of each epoch
    validation_data=(x_val, y_val),
)

It's good practice to go through the documentation once or twice: Training and evaluation with the built-in methods

CodePudding user response：

If you're going to use validation_split to create your validation set, it is easier in my experience, if done when you create your data generator.

train_datagen = ImageDataGenerator(validation_split=0.1)
test_datagen = ImageDataGenerator()

You then use your data generator, either with flow, or flow_from_directory, using your train data.

train_iter = train_datagen.flow(X_train, Y_train, batch_size=6, subset="training")
val_iter = train_datagen.flow(X_train, Y_train, batch_size=6, subset="validation")
test_iter = test_datagen.flow(X_test, Y_test, batch_size=6, shuffle=False)

Then .fit your model using train_iter as x-input and val_iter as input for validation_data.

model.fit(train_iter, steps_per_epoch=len(train_iter), validation_data=val_iter, validation_steps=len(val_iter), epochs=5, shuffle=True)

Then use test_iter to evaluate/predict.

model.evaluate(test_iter, steps=len(test_iter))
model.predict(test_iter, steps=len(test_iter))