train.py

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

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

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

# 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(512))
model.add(Activation("relu"))

model.add(Dense(64))
model.add(Activation("relu"))

# The output layer with 13 neurons, for 13 classes
model.add(Dense(13))
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 40 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=10, validation_split=0.1)

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

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

model.save('datasets/CNN.model')

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