final-code-static.py

# Part 1 - Building the CNN
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten,Dropout

# Initialing the CNN
classifier = Sequential()

# Step 1 - Convolutio Layer 
classifier.add(Conv2D(32, 3,  3, input_shape = (64, 64, 3), activation = 'relu'))

#step 2 - Pooling
classifier.add(MaxPool2D(pool_size =(2,2)))

# Adding second convolution layer
classifier.add(Conv2D(32, 3,  3, activation = 'relu'))
classifier.add(MaxPool2D(pool_size =(2,2)))

#Adding 3rd Concolution Layer
classifier.add(Conv2D(64, 3,  3, activation = 'relu'))
classifier.add(MaxPool2D(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 Fittting the CNN to the image
from tensorflow.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=25,
        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()