import tensorflow as tf
minist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = minist.load_data()
x_train, x_test = x_train / 255, x_test / 255
print(x_train.shape)
print(x_train)
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,
    28)), tf.keras.layers.Dense(512, activation=tf.nn.relu), tf.keras.
    layers.Dropout(0.2), 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=10)
model.evaluate(x_test, y_test)

2483a8e44f077200a44f335b8102c41edfb55362
<mask token>

<mask token>
print(x_train.shape)
print(x_train)
<mask token>
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10)
model.evaluate(x_test, y_test)

<mask token>
minist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = minist.load_data()
x_train, x_test = x_train / 255, x_test / 255
print(x_train.shape)
print(x_train)
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,
    28)), tf.keras.layers.Dense(512, activation=tf.nn.relu), tf.keras.
    layers.Dropout(0.2), 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=10)
model.evaluate(x_test, y_test)

import tensorflow as tf
minist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = minist.load_data()
x_train, x_test = x_train / 255, x_test / 255
print(x_train.shape)
print(x_train)
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,
    28)), tf.keras.layers.Dense(512, activation=tf.nn.relu), tf.keras.
    layers.Dropout(0.2), 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=10)
model.evaluate(x_test, y_test)
