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# # Train your first neural network: basic classification

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
print(tf.__version__)

# ### Import the fashion MNIST dataset

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']

# ### Explore the data

train_images.shape

len(train_labels)

train_labels

# +
#train_images[1]

# +
# #%matplotlib inline
#import matplotlib.image as mpimg
#imgplot = plt.imshow(train_images[1])
# -

test_images.shape

len(test_labels)

# ### Preprocess the data

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

# ### setup the layers

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

# ### Train the model

model.fit(train_images,train_labels,epochs=2)

# ### Evaluate accuracy

test_loss,test_acc = model.evaluate(test_images,test_labels)
print('Test accuracy:', test_acc)

# ### Make predictions

predictions = model.predict(test_images)

predictions[0]

np.argmax(predictions[0])

test_labels[0]


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 = '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[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('blue')
        


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()

num_rows = 5
num_cols = 5
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()

image_no = 0
img = test_images[image_no]
print(img.shape)

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])
print(prediction_result)



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