cnn_trial.py

# Importing the Keras libraries and packages
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
import numpy as np
from keras.preprocessing import image
import seaborn as sns
import matplotlib.pyplot as plt
from keras.callbacks import TensorBoard

tensorboard = TensorBoard(log_dir='./graph', histogram_freq=0,
                          write_graph=True, write_images=False)
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)

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('data/cnn/CNN_Data/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('data/cnn/CNN_Data/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')

classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000,
callbacks=[tensorboard])

'''
sns.set_style("darkgrid")
font = {'family' : 'arial',
        'weight' : 'normal',
        'size'   : 14}

plt.rc('font', **font)
plt.figure(figsize=(15,12))
plt.plot(classifier.history['loss'], linewidth=3)
plt.plot(classifier.history['val_loss'])
plt.title('Deep Learning Model Loss (RMSE): Regression')
plt.ylabel('RMSE')
plt.xlabel('Epochs')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.savefig('dl_base_rmse.png')

# Part 3 - Making new predictions

test_image = image.load_img('data/cnn/CNN_Data/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
	prediction = 'dog'
else:
	prediction = 'cat'
print(prediction)
'''