<jupyter_start><jupyter_text>Model<jupyter_code># 256 hidden units and applies the ReLU activation function, output layer
net = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dense(10)])
!pip uninstall matplotlib
!pip install --upgrade matplotlib
# The training loop
batch_size, lr, num_epochs = 256, 0.1, 10
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
trainer = tf.keras.optimizers.SGD(learning_rate=lr)

train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)<jupyter_output><empty_output>
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