# Example of Convolutional Neural Networks

#%%
# Importing moduls
import tensorflow as tf

from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

#%%
#  LOAD AND SPLIT DATASET
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

# %%
# Let's look at a one image
IMG_INDEX = 7  # change this to look at other images

plt.imshow(train_images[IMG_INDEX] ,cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[IMG_INDEX][0]])
plt.show()

# %%
# Bulidng a Model
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

# %%
model.summary()

# %%
# Adding Dense Layers
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))

# %%
model.summary()

# %%
# Training
model.compile(optimizer='adam',
              loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics = ['accuracy'])

history = model.fit(train_images, train_labels, epochs=1, 
                    validation_data=(test_images, test_labels))

# %%
# Evaluating the Model
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print(test_acc)