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Well i am trying to train the model , unfortunately i keep ending up with this ValueError. How should i approach to fix this? should i use numpy.resize or cv2.resize to change the dimensions to (3,150,150). If so , where would i resize is it in the generator?

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".