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import tensorflow as tf
import tensorflow

from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

fashion_mnist=keras.datasets.fashion_mnist

import os
print(os.getcwd())

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Ankle boot','Bag']

train_images.shape

len(train_labels)

test_images.shape

len(train_labels)

train_labels

#### now we have to preprocess this data so now we are try to load an image krishna's writes
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

#    Let’s display some images.
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)#for unshowing the grid lines in the images 
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()
# where are displaying classes name below

#    Setup the layers
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

# +
#    Compile the Model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

 
# -

#    Model Training
model.fit(train_images, train_labels, epochs=10)

#    Evaluating Accuracy
test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)

#    Making Predictions
predictions = model.predict(test_images)

predictions[0]

#A prediction is an array of 10 numbers. 
#These describe the “confidence” of the model that the image corresponds to each of the 10 different articles of clothing. 
#We can see which label has the highest confidence value.
np.argmax(predictions[0])
#Model is most confident that it's an ankle boot. Let's see if it's correct

test_labels[0]


#    Now, it’s time to look at the full set of 10 channels
def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array[i], 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 = 'green'
  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[i], true_label[i]
  plt.grid(False)
  plt.xticks([])
  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('green')


#    Let’s look at the 0th and 10th image first
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()

# +
i = 10
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()


# -

#Now, let’s plot several images and their predictions. Correct ones are green, while the incorrect ones are 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, test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions, test_labels)
plt.show()

# +
#    Finally, we will use the trained model to make a prediction about a single image.
# Grab an image from the test dataset
img = test_images[0]

print(img.shape)

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

print(img.shape)

predictions_single = model.predict(img) 
print(predictions_single)
# -

plot_value_array(0, predictions_single, test_labels)
plt.xticks(range(10), class_names, rotation=45)
plt.show()

prediction_result = np.argmax(predictions_single[0])

prediction_result












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