training.py

# Step 1: Import the required packages
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
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

# Step 2: Initialising the CNN
model = Sequential()

# Step 3: Convolution
model.add(Conv2D(32, (3, 3), input_shape=(50, 50, 3), activation='relu'))

# Step 4: Pooling
model.add(MaxPooling2D(pool_size=(2, 2)))

# Step 5: Second convolutional layer
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# Step 6: Flattening
model.add(Flatten())

# Step 7: Full connection
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

# Step 8: Compiling the CNN
model.compile(optimizer='adam', loss='binary_crossentropy',
              metrics=['accuracy'])

# Step 9: ImageDataGenerator

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

# Step 10: Load the training Set
training_set = train_datagen.flow_from_directory('./training_set/training_set/',
                                                 target_size=(50, 50),
                                                 batch_size=32,
                                                 class_mode='binary')
# Step 11: Classifier Training
history = model.fit_generator(training_set,
                              steps_per_epoch=4000,
                              epochs=10,
                              validation_steps=100)

plt.plot(history.history['accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Step 12: Convert the Model to json
model_json = model.to_json()
with open("./model.json", "w") as json_file:
    json_file.write(model_json)

# Step 13: Save the weights in a seperate file
model.save_weights("./model.h5")

print("Classifier trained Successfully!")