CNN.py

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import pickle
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.models import model_from_json
from keras.models import load_model

#Opening the files about the data
X = pickle.load(open("X.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))

# normalizing data (a pixel goes from 0 to 255)
X = X/255.0

print(X)

# Building the model
model = Sequential()
# 3 convolutional layers
model.add(Conv2D(32, (3, 3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

# 2 hidden layers
model.add(Flatten())
model.add(Dense(128))
model.add(Activation("relu"))

model.add(Dense(128))
model.add(Activation("relu"))

# The output layer with 3 neurons, for 3 classes
model.add(Dense(3))
model.add(Activation("softmax"))

# Compiling the model using some basic parameters
model.compile(loss="sparse_categorical_crossentropy",
				optimizer="adam",
				metrics=["accuracy"])

# Training the model, with 80 iterations
# validation_split corresponds to the percentage of images used for the validation phase compared to all the images
history = model.fit(X, y, batch_size=32, epochs=80, validation_split=0.1)

# Saving the model
model_json = model.to_json()
with open("model.json", "w") as json_file :
	json_file.write(model_json)

model.save_weights("model.h5")
print("Saved model to disk")

model.save('CNN.model')

# Printing a graph showing the accuracy changes during the training phase
print(history.history.keys())
plt.figure(1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')

#scores = model.evaluate(X_test, Y_test, verbose=0)
#print("Accuracy: %.2f%%" % (scores[1]*100))