Optimization.py

import tensorflow.keras as keras
import tensorflow as tf
import matplotlib.pyplot as plt


mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()



plt.imshow(x_train[0],cmap=plt.cm.binary)
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))


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

model.fit(x_train, y_train, epochs=3)

val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss)
print(val_acc)


(x_train, y_train), (x_test, y_test) = mnist.load_data()   
x_train = tf.keras.utils.normalize(x_train, axis=1).reshape(x_train.shape[0], -1)
x_test = tf.keras.utils.normalize(x_test, axis=1).reshape(x_test.shape[0], -1)

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu, input_shape= x_train.shape[1:]))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))


model.x_train()