!pip install tensorflow

import tensorflow as tf print(tf.__version__)

import tensorflow as tf mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data() 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.Dropout(0.2)) model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))

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

model.fit(x_train, y_train, epochs=30) model.evaluate(x_test, y_test)

import matplotlib.pyplot as plt

plt.figure(figsize=(30,18)) for i in range(20): plt.subplot(6,6,i+1) plt.imshow(x_train[i], cmap=plt.cm.binary) plt.xlabel(y_train[i])