# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import TensorBoard

print(tf.__version__)

#Import and load the Fashion MNIST data directly from TensorFlow
fashion_mnist = keras.datasets.fashion_mnist
#train_images and train_labels arrays are the training set
(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']
# print('Train image :' , train_images.shape)
# print('Train label count : %s' % len(train_labels))
# print('Test image :' ,test_images.shape)
# print('Test label count : %s' %len(test_labels))

# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()

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()

#Build the model
#Set up the layers
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

#for tensorboard
tbCallBack = TensorBoard(log_dir='logs/MNIST',  
                 histogram_freq=0,  
#                  batch_size=32,     
                 write_graph=True,  
                 write_grads=True, 
                 write_images=True,
                 embeddings_freq=0, 
                 embeddings_layer_names=None, 
                 embeddings_metadata=None)  

# #training data
# model.fit(train_images, train_labels, epochs=10, callbacks=[tbCallBack])

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

# #print('\nTest accuracy:', test_acc)


predictions = model.predict(test_images)
np.argmax(predictions[0])
test_labels[0]

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.xticks(np.arange(5),('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'))
  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), class_names, rotation=30 , fontsize=8)
  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')

def show_plot_image(i, predictions_array):
  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.xticks(range(10), class_names, rotation=45)

  plt.show()


#print single predictions result
show_plot_image(0, predictions)


# # show multi predictions result
# # Color correct predictions in blue and incorrect predictions in red.
# 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)
#   print('predit %s' %i ,predictions[i])
# plt.tight_layout()
# plt.show()

730ac34d1d3f0f4faa7b928d576e80df8d54d38c
<mask token>


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')


def show_plot_image(i, predictions_array):
    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()


<mask token>

<mask token>
print(tf.__version__)
<mask token>
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])
<mask token>
np.argmax(predictions[0])
test_labels[0]


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')


def show_plot_image(i, predictions_array):
    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()


show_plot_image(0, predictions)

<mask token>
print(tf.__version__)
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']
train_images = train_images / 255.0
test_images = test_images / 255.0
model = keras.Sequential([keras.layers.Flatten(input_shape=(28, 28)), keras
    .layers.Dense(128, activation='relu'), keras.layers.Dense(10,
    activation='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])
tbCallBack = TensorBoard(log_dir='logs/MNIST', histogram_freq=0,
    write_graph=True, write_grads=True, write_images=True, embeddings_freq=
    0, embeddings_layer_names=None, embeddings_metadata=None)
predictions = model.predict(test_images)
np.argmax(predictions[0])
test_labels[0]


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')


def show_plot_image(i, predictions_array):
    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()


show_plot_image(0, predictions)

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import TensorBoard
print(tf.__version__)
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']
train_images = train_images / 255.0
test_images = test_images / 255.0
model = keras.Sequential([keras.layers.Flatten(input_shape=(28, 28)), keras
    .layers.Dense(128, activation='relu'), keras.layers.Dense(10,
    activation='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])
tbCallBack = TensorBoard(log_dir='logs/MNIST', histogram_freq=0,
    write_graph=True, write_grads=True, write_images=True, embeddings_freq=
    0, embeddings_layer_names=None, embeddings_metadata=None)
predictions = model.predict(test_images)
np.argmax(predictions[0])
test_labels[0]


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')


def show_plot_image(i, predictions_array):
    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()


show_plot_image(0, predictions)

# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import TensorBoard

print(tf.__version__)

#Import and load the Fashion MNIST data directly from TensorFlow
fashion_mnist = keras.datasets.fashion_mnist
#train_images and train_labels arrays are the training set
(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']
# print('Train image :' , train_images.shape)
# print('Train label count : %s' % len(train_labels))
# print('Test image :' ,test_images.shape)
# print('Test label count : %s' %len(test_labels))

# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()

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()

#Build the model
#Set up the layers
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

#for tensorboard
tbCallBack = TensorBoard(log_dir='logs/MNIST',  
                 histogram_freq=0,  
#                  batch_size=32,     
                 write_graph=True,  
                 write_grads=True, 
                 write_images=True,
                 embeddings_freq=0, 
                 embeddings_layer_names=None, 
                 embeddings_metadata=None)  

# #training data
# model.fit(train_images, train_labels, epochs=10, callbacks=[tbCallBack])

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

# #print('\nTest accuracy:', test_acc)


predictions = model.predict(test_images)
np.argmax(predictions[0])
test_labels[0]

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.xticks(np.arange(5),('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'))
  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), class_names, rotation=30 , fontsize=8)
  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')

def show_plot_image(i, predictions_array):
  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.xticks(range(10), class_names, rotation=45)

  plt.show()


#print single predictions result
show_plot_image(0, predictions)


# # show multi predictions result
# # Color correct predictions in blue and incorrect predictions in red.
# 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)
#   print('predit %s' %i ,predictions[i])
# plt.tight_layout()
# plt.show()
