import tensorflow as tf
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()




import matplotlib.pyplot as plt
image_index = 7485 # You may select anything up to 60,000
print(y_test[image_index])
plt.imshow(x_test[image_index], cmap='Greys')



x_train.shape




# Reformulando o array para funcinar com o keras
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
# vendo se os valores sao float para termos divisoes depois da virgula
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# Filtrando os codigos RGB dividindo por 255
x_train /= 255
x_test /= 255
print('formato do x_train :', x_train.shape)
print('numero de imagens em x_train', x_train.shape[0])
print('Numero de imagens em x_test', x_test.shape[0])




# Keras
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
# Criando model sequencial e adicionando as layers
model = Sequential()
model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation=tf.nn.relu))
model.add(Dropout(0.2))
model.add(Dense(10,activation=tf.nn.softmax)) # a ultima layer tera 10 digitos possiveis




model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy', 
              metrics=['accuracy'])
model.fit(x=x_train,y=y_train, epochs=1)


model.evaluate(x_test, y_test)


image_index =  7485   # 0
plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')
pred = model.predict(x_test[image_index].reshape(1, 28, 28, 1))

print("O computador leu : " pred.argmax())
