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I am trying to train the model, I keep ending up with this ValueError:

ValueError: Error when checking input: expected conv2d_13_input to have shape (3, 150, 150) but got array with shape (150, 150, 3)

How can I fix this? Should I use numpy.resize or cv2.resize to change the dimensions to (3,150,150). If so, how would I resize it in the generator?

Here is my code:

train_datagen = ImageDataGenerator(

    rescale=1./255,

    shear_range=0.2,

    zoom_range=0.2)



test_datagen = ImageDataGenerator(rescale=1./255)



from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D

from keras.layers import Activation, Dropout, Flatten, Dense



model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150),padding='same'))

model.add(Activation('relu')) 

model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))



model.add(Conv2D(32, (3, 3),padding='same'))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))



model.add(Conv2D(64, (3, 3),padding='same'))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))



model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

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'])



batch_size = 64



# this is a generator that will read pictures found in

# subfolers of 'data/train', and indefinitely generate

# batches of augmented image data

train_generator = train_datagen.flow_from_directory(

    'C:\Users\Zahid\Desktop\Dataset\train',  # this is the target directory

    target_size=(150, 150),  # all images will be resized to 150x150

    batch_size=batch_size,

    color_mode='rgb',

    class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels



# this is a similar generator, for validation data

validation_generator = test_datagen.flow_from_directory(

    'C:\Users\Zahid\Desktop\Dataset\val',

    target_size=(150, 150),

    batch_size=batch_size,

    color_mode='rgb',

    class_mode='binary')



model.fit_generator(

    train_generator,

    steps_per_epoch=2000 // batch_size,

    epochs=50,

    validation_data=validation_generator,

    validation_steps=800 // batch_size)

model.save_weights('first_try.h5')

Change this:

model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150),padding='same'))

to this:

model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3),padding='same'))

And read the doc: https://keras.io/layers/convolutional/#conv2d

In particular the section about data_format:

data_format: A string, one of "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".