# from https://www.tensorflow.org/tutorials/images/cnn

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

import sys
sys.path.append('../hostLib/')
from hostLib.layers.conv2d import Conv2D as Conv2DFPGA

(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']

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    # The CIFAR labels happen to be arrays, 
    # which is why you need the extra index
    plt.xlabel(class_names[train_labels[i][0]])
plt.show()

model = models.Sequential()
model.add(layers.Lambda(lambda x: tf.image.resize(x, (228,228)))) #to-do: implement 3 stage 32x32_3x3 conv2d with relu
model.add(Conv2DFPGA(1))#32
model.add(layers.Activation('relu'))
model.add(layers.Lambda(lambda x: tf.image.resize(x, (228,228))))
model.add(Conv2DFPGA(1))#64
model.add(layers.Activation('relu'))
model.add(layers.Lambda(lambda x: tf.image.resize(x, (228,228))))
model.add(Conv2DFPGA(1))#64
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(16, 16)))

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))

model.build(input_shape=(None, 32, 32, 3))
model.summary()

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

history = model.fit(train_images, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print(test_acc)
plt.show()