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Cat and dog classification

 



# %%  Importing the libraries

from keras.models import Sequential
from keras.layers import Conv2D 
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# %%  Initializing the CNN

classifier = Sequential()

# %%  Step-1 Convolution

classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# All the images come to us does'nt have same size and shape so we have to fix the
 dimensions of the pixels rgb color channel in input shape parameter

# %%  Step - 2 Pooling

classifier.add(MaxPooling2D(pool_size=(2, 2))) 

# %%  Adding a second convolution layer

#classifier.add(Convolution2D(32, 3, 3, activation='relu'))
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3),  activation = 'relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))

# %%  Step-3 Flattening

classifier.add(Flatten())


# %%  Step-4 Full Connection

# output_dim = random (mostly give it according to input variables but here those are
 too many)

classifier.add(Dense(units=68, activation='relu'))

classifier.add(Dense(units=120, activation='relu'))

# output layer
#classifier.add(Dense(output_dim=1, activation='sigmoid'))
classifier.add(Dense(units=1, activation='sigmoid'))

# %%  Compiling CNN

# adam -> stochastic gradient descent
# binary -> as we have only two categories otherwise 'categorical_crossentropy'
classifier.compile(optimizer='adam', loss="binary_crossentropy", metrics=['accuracy'])


# %%  Fitting CNN to images


from keras.preprocessing.image import ImageDataGenerator

# %%  Rescaling for feature scaling

train_datagen = ImageDataGenerator(rescale=1./255,
                                    shear_range=0.2,
                                    zoom_range=0.2,
                                    horizontal_flip=True) 


test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size=(64, 64),
                                                 batch_size=32,
                                                 class_mode='binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size=(64, 64),
                                            batch_size=32,
                                            class_mode='binary')

classifier.fit_generator(training_set,
               steps_per_epoch=8000,
               epochs=2,
               validation_data=test_set,
               validation_steps=2000)




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