Deep-Learning-with-TensorFlow-book/ch09-过拟合/dropout.py

import  os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import  tensorflow as tf
from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics


def preprocess(x, y):

    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)

    return x,y


batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())



db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz) 




network = Sequential([layers.Dense(256, activation='relu'),
                     layers.Dropout(0.5), # 0.5 rate to drop
                     layers.Dense(128, activation='relu'),
                     layers.Dropout(0.5), # 0.5 rate to drop
                     layers.Dense(64, activation='relu'),
                     layers.Dense(32, activation='relu'),
                     layers.Dense(10)])
network.build(input_shape=(None, 28*28))
network.summary()

optimizer = optimizers.Adam(lr=0.01)



for step, (x,y) in enumerate(db):

    with tf.GradientTape() as tape:
        # [b, 28, 28] => [b, 784]
        x = tf.reshape(x, (-1, 28*28))
        # [b, 784] => [b, 10]
        out = network(x, training=True)
        # [b] => [b, 10]
        y_onehot = tf.one_hot(y, depth=10) 
        # [b]
        loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, out, from_logits=True))


        loss_regularization = []
        for p in network.trainable_variables:
            loss_regularization.append(tf.nn.l2_loss(p))
        loss_regularization = tf.reduce_sum(tf.stack(loss_regularization))

        loss = loss + 0.0001 * loss_regularization
 

    grads = tape.gradient(loss, network.trainable_variables)
    optimizer.apply_gradients(zip(grads, network.trainable_variables))


    if step % 100 == 0:

        print(step, 'loss:', float(loss), 'loss_regularization:', float(loss_regularization)) 


    # evaluate
    if step % 500 == 0:
        total, total_correct = 0., 0

        for step, (x, y) in enumerate(ds_val): 
            # [b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # [b, 784] => [b, 10] 
            out = network(x, training=True)  
            # [b, 10] => [b] 
            pred = tf.argmax(out, axis=1) 
            pred = tf.cast(pred, dtype=tf.int32)
            # bool type 
            correct = tf.equal(pred, y)
            # bool tensor => int tensor => numpy
            total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()
            total += x.shape[0]

        print(step, 'Evaluate Acc with drop:', total_correct/total)

        total, total_correct = 0., 0

        for step, (x, y) in enumerate(ds_val): 
            # [b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # [b, 784] => [b, 10] 
            out = network(x, training=False)  
            # [b, 10] => [b] 
            pred = tf.argmax(out, axis=1) 
            pred = tf.cast(pred, dtype=tf.int32)
            # bool type 
            correct = tf.equal(pred, y)
            # bool tensor => int tensor => numpy
            total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()
            total += x.shape[0]

        print(step, 'Evaluate Acc without drop:', total_correct/total)