import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib
matplotlib.use('qt5agg')
import matplotlib.pyplot as plt


def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array, true_label[i], img[i]
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)

    predicted_label = np.argmax(predictions_array)
    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'

    plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)


def plot_value_array(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array, true_label[i]
    plt.grid(False)
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(range(10), predictions_array, color="#777777")
    plt.ylim([0, 1])
    predicted_label = np.argmax(predictions_array)

    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')



fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()
# # plt.savefig('demo.png', bbox_inches='tight')

train_images = train_images / 255.0
test_images = test_images / 255.0

# 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)
#     plt.xlabel(class_names[train_labels[i]])
# plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

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

model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
print('\nTest loss:', test_loss)

probability_model = tf.keras.Sequential([model,
                                         tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)

# print(predictions[1])
# print(np.argmax(predictions[1]))
# print("the predict label is: ", class_names[np.argmax(predictions[1])])
# print("the actual label is: ", class_names[test_labels[1]])
# plt.figure()
# plt.imshow(test_images[1])
# plt.colorbar()
# plt.grid(False)
# plt.show()

# i = 0
# plt.figure(figsize=(6,3))
# plt.subplot(1,2,1)
# plot_image(i, predictions[i], test_labels, test_images)
# plt.subplot(1,2,2)
# plot_value_array(i, predictions[i],  test_labels)
# plt.show()

num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions[i], test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()






import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib
matplotlib.use('qt5agg')
import matplotlib.pyplot as plt


def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array, true_label[i], img[i]
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)

    predicted_label = np.argmax(predictions_array)
    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'

    plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)


def plot_value_array(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array, true_label[i]
    plt.grid(False)
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(range(10), predictions_array, color="#777777")
    plt.ylim([0, 1])
    predicted_label = np.argmax(predictions_array)

    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')



fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()
# # plt.savefig('demo.png', bbox_inches='tight')

train_images = train_images / 255.0
test_images = test_images / 255.0

# 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)
#     plt.xlabel(class_names[train_labels[i]])
# plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

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

model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
print('\nTest loss:', test_loss)

probability_model = tf.keras.Sequential([model,
                                         tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)

# print(predictions[1])
# print(np.argmax(predictions[1]))
# print("the predict label is: ", class_names[np.argmax(predictions[1])])
# print("the actual label is: ", class_names[test_labels[1]])
# plt.figure()
# plt.imshow(test_images[1])
# plt.colorbar()
# plt.grid(False)
# plt.show()

# i = 0
# plt.figure(figsize=(6,3))
# plt.subplot(1,2,1)
# plot_image(i, predictions[i], test_labels, test_images)
# plt.subplot(1,2,2)
# plot_value_array(i, predictions[i],  test_labels)
# plt.show()

num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions[i], test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()






# plt.figure() # plt.imshow(train_images[0]) # plt.colorbar() # plt.grid(False) # plt.show() # # plt.savefig('demo.png', bbox_inches='tight') # 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) #     plt.xlabel(class_names[train_labels[i]]) # plt.show() # print(predictions[1]) # print(np.argmax(predictions[1])) # print("the predict label is: ", class_names[np.argmax(predictions[1])]) # print("the actual label is: ", class_names[test_labels[1]]) # plt.figure() # plt.imshow(test_images[1]) # plt.colorbar() # plt.grid(False) # plt.show() # i = 0 # plt.figure(figsize=(6,3)) # plt.subplot(1,2,1) # plot_image(i, predictions[i], test_labels, test_images) # plt.subplot(1,2,2) # plot_value_array(i, predictions[i],  test_labels) # plt.show()