# Convolutional Neural Network

# Installing Keras

# Enter the following command in a terminal (or anaconda prompt for Windows users): conda install -c conda-forge keras

# Part 1 - Building the CNN

# 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

from keras.layers import Dropout

"""batches per weights updated"""

batch_sizeTrain = 16

'''what are the choices of possible images'''

num_classes = 2

'''how many times should it repeat'''

epochs = 100

'''dimensions of the images'''

img_rows, img_cols = 64, 64

"""Size"""

TrainSize = 606

TestSize = 140

# Initialising the CNN

classifier = Sequential()

# Step 1 - Convolution

classifier.add(Conv2D(32, (3, 3), input_shape = (img_rows, img_cols, 1), activation = 'relu'))

classifier.add(Dropout(rate = 0.3))

# Step 2 - Pooling

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

# Adding a third convolutional layer

classifier.add(Conv2D(64, (3, 3), activation='relu'))

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

classifier.add(Dropout(rate = 0.3))

# Step 3 - Flattening

classifier.add(Flatten())

# Step 4 - Full connection

classifier.add(Dense(units = 64, activation = 'relu'))

classifier.add(Dropout(rate = 0.3))

classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN

classifier.compile(optimizer = 'adadelta', 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 = False)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('TrainingSet',

color_mode = 'grayscale')

test_set = test_datagen.flow_from_directory('TestSet',

color_mode = 'grayscale')

classifier.fit_generator(training_set,

steps_per_epoch = TrainSize/batch_sizeTrain,

epochs = epochs,

validation_data = test_set,

validation_steps = 1)

"""def runClassifier(model, batchSize, epoch):

steps_per_epoch = TrainSize/batchSize

model.fit_generator(training_set,

steps_per_epoch = TrainSize/batchSize,

epochs = epoch,

validation_data = test_set,

validation_steps = TestSize/7)

f = open("Log.txt", "a")

log = "Batch Size: " + str(batchSize) + ", Epoch: " + str(epoch)

f.write(log)

f.write("\n")

print("-------------------------------------------------------------------------------------------------")"""