<jupyter_start><jupyter_text>[View in Colaboratory](https://colab.research.google.com/github/mayankprhr/fashionmnist1/blob/master/Classify_Wardrobe.ipynb)<jupyter_code># 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__)
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.shape
(60000, 28, 28)

len(train_labels)

train_labels
test_images.shape

len(test_labels)
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
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]])
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)
])
model.compile(optimizer=tf.train.AdamOptimizer(),
             loss='sparse_categorical_crossentropy',
             metrics=['accuracy'])
model.fit(train_images,train_labels,epochs=5)
test_loss,test_acc=model.evaluate(test_images,test_labels)
print('Test accuracy:',test_acc)
predictions=model.predict(test_images)
predictions[0]

np.argmax(predictions[0])
test_labels[12]

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')
  
#looking at one of the images

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)


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)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14367633004141447, 'fraction_numerical': 0.02453010512902198, 'mean_word_length': 5.487603305785124, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '661200', 'license': 'MIT', 'n_tokens_mistral': 1244, 'n_tokens_neox': 1189, 'n_words': 145, 'path': 'Classify_Wardrobe.ipynb', 'repo_name': 'mayankprhr/fashionmnist1', 'size': '3139'}