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

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')