process.py

import tensorflow as tf
import numpy as np

from tensorflow.keras.datasets.fashion_mnist import load_data

#trying to use the gpu power
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
print(tf.config.list_physical_devices('GPU'))

dataset = load_data()
(x_train, y_train), (x_test, y_test) = dataset

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

#convert values to between 0 and 1
x_train = x_train / 255.0
x_test = x_test/255.0

#instansitate the model by stacking the layers on top of each other
model = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28,28)),
  tf.keras.layers.Dense(128, activation='relu'),  #create network of 128 neurons
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10) #create network of 10 
])

#create the model by passing in an optimizer, loss function and metric
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

#fit the model with the data
model.fit(x_train, y_train, epochs=10)

#test the model
test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=2)

probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()   #logits to find probabilities
])

predictions = probability_model.predict(x_test)
predicted_label = np.argmax(predictions) #select max from the values returned
print(y_test[0]) #actual labels for the values

def plot_image(i, predictions_array, true_label, img):
  true_label, img = 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):
  true_label = 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')

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], y_test, x_test)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions[i], y_test)
plt.tight_layout()
plt.show()