# Part 1 - Building the CNN
#importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense, Dropout
from sklearn import metrics
from keras import optimizers
import numpy as np

# Initialing the CNN
classifier = Sequential()

# Step 1 - Convolution Layer 
classifier.add(Convolution2D(32, 3,  3, input_shape = (64, 64, 3), activation = 'relu'))
#classifier.add(Activation = 'relu')
#step 2 - Pooling
classifier.add(MaxPooling2D(pool_size =(2,2)))

# Adding second convolution layer
classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))

#Adding 3rd Concolution Layer
classifier.add(Convolution2D(64, 3,  3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))


#Step 3 - Flattening
classifier.add(Flatten())

#Step 4 - Full Connection
classifier.add(Dense(256, activation = 'relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(26, activation = 'softmax'))

#Compiling The CNN
classifier.compile(
              optimizer = optimizers.SGD(lr = 0.01),
              loss = 'categorical_crossentropy',
              metrics = ['accuracy'])

#Part 2 Fitting the CNN to the image
from keras.preprocessing.image import ImageDataGenerator
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(
        'mydata/training_set',
        target_size=(64, 64),
        batch_size=32,
        class_mode='categorical')

test_set = test_datagen.flow_from_directory(
        'mydata/test_set',
        target_size=(64, 64),
        batch_size=32,
        class_mode='categorical')

model = classifier.fit_generator(
        training_set,
        steps_per_epoch=800,
        epochs=1,
        validation_data = test_set,
        validation_steps = 6500
      )

'''#Saving the model
import h5py
classifier.save('Trained_model.h5')'''

print(model.history.keys())
import matplotlib.pyplot as plt
# summarize history for accuracy
plt.plot(model.history['acc'])
plt.plot(model.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

# summarize history for loss
plt.plot(model.history['loss'])
plt.plot(model.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

# confusion matrix

test_steps_per_epoch = np.math.ceil(test_set.samples / test_set.batch_size)
model = model()
predictions = model.predict_generator(test_set, steps=test_steps_per_epoch)
# Get most likely class
predicted_classes = np.argmax(predictions, axis=1)

true_classes = test_set.classes
class_labels = list(test_set.class_indices.keys())   

report = metrics.classification_report(true_classes, predicted_classes, target_names=class_labels)
print(report)