tensorflow - Epoch 需要越来越多的时间

Question

我可能有一个“臃肿的图表”，请参阅( Why does tf.assign() slow the execution time? )，因为每个纪元都花费越来越多的时间，但我在代码中看不到它。你能帮助我吗，我还是一个 Tensorflow 新手。

# NEURAL NETWORK
def MLP(x, weights, biases, is_training):

    # Hiden layer 1
    hLayer1 = tf.add(tf.matmul(x, weights["w1"]), biases["b1"])
    hLayer1 = tf.nn.sigmoid(hLayer1)
    bn1 = batch_norm_wrapper(hLayer1, gamma=weights["gamma1"], beta=weights["beta1"], is_training=is_training, name="1")
    hLayer1 = bn1


    # Hiden layer 2
    hLayer2 = tf.add(tf.matmul(hLayer1, weights["w2"]), biases["b2"])
    hLayer2 = tf.nn.sigmoid(hLayer2)
    bn2 = batch_norm_wrapper(hLayer2, gamma=weights["gamma2"], beta=weights["beta2"], is_training=is_training, name="2")
    hLayer2 = bn2

    # Output layer
    outLayer = tf.add(tf.matmul(hLayer2, weights["wOut"]), biases["bOut"], name="outLayer")

    return outLayer



# Weights and biases
weights = {
    "w1": tf.get_variable(shape=[n_input, n_hLayer1], initializer=tf.keras.initializers.he_normal(seed=5), name="w1", trainable=True),
    "w2": tf.get_variable(shape=[n_hLayer1, n_hLayer2], initializer=tf.keras.initializers.he_normal(seed=5), name="w2", trainable=True),
    "wOut": tf.get_variable(shape=[n_hLayer2, n_classes], initializer=tf.keras.initializers.he_normal(seed=5), name="wOut", trainable=True),

    "gamma1": tf.get_variable(shape=[n_hLayer1], initializer=tf.ones_initializer(), name="gamma1", trainable=True),
    "beta1": tf.get_variable(shape=[n_hLayer1], initializer=tf.zeros_initializer(), name="beta1", trainable=True),

    "gamma2":tf.get_variable(shape=[n_hLayer2], initializer=tf.ones_initializer(), name="gamma2", trainable=True),
    "beta2": tf.get_variable(shape=[n_hLayer2], initializer=tf.zeros_initializer(), name="beta2", trainable=True)
}

biases = {
    "b1": tf.get_variable(shape=[n_hLayer1], initializer=tf.zeros_initializer(), name="b1", trainable=True),
    "b2": tf.get_variable(shape=[n_hLayer2], initializer=tf.zeros_initializer(), name="b2", trainable=True),
    "bOut": tf.get_variable(shape=[n_classes], initializer=tf.zeros_initializer(), name="bOut", trainable=True)
}


def batch_norm_wrapper(inputs, gamma, beta,  is_training, name, decay=0.999):

    pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), name="pop_mean{}".format(name), trainable=False)
    pop_var = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), name="pop_var{}".format(name), trainable=False)

    if is_training:
        batch_mean, batch_var = tf.nn.moments(inputs, [0])

        train_mean = tf.assign(pop_mean, pop_mean*decay + batch_mean*(1-decay))
        train_var = tf.assign(pop_var, pop_var*decay + batch_var*(1-decay))


        with tf.control_dependencies([train_mean, train_var]):
            return tf.nn.batch_normalization(x=inputs, mean=batch_mean, variance=batch_var, scale=gamma, offset=beta, variance_epsilon=0.001)

    else:
        return tf.nn.batch_normalization(x=inputs, mean=pop_mean, variance=pop_var, scale=gamma, offset=beta, variance_epsilon=0.001)


# Model
predictions = MLP(next_element[0], weights, biases, is_training=True)


# Loss function and regularization
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=predictions, labels=next_element[1]))
l1_regularizer = tf.reduce_sum(tf.abs(weights["w1"])) + tf.reduce_sum(tf.abs(weights["w2"])) + tf.reduce_sum(tf.abs(weights["wOut"]))
l2_regularizer = tf.reduce_mean(tf.nn.l2_loss(weights["w1"]) + tf.nn.l2_loss(weights["w2"]) + tf.nn.l2_loss(weights["wOut"]))
loss = loss + r*alpha1*l1_regularizer + (1-r)*alpha2*l2_regularizer

# Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)



# LAUNCH THE GRAPH
with tf.Session() as sess:

    sess.run(init_op)

    # Training
    for trainEpoch in range(training_epochs):

        sess.run(training_iterator_op)

        while True:

            try:
                value = sess.run(next_element)
                sess.run([loss, optimizer])

            except tf.errors.OutOfRangeError:
                break

我使用数据集 API 来运行我的训练数据。

Answer 1

我想分享我在 TensorFlow 2.x.x(在我的例子中为 2.4.1)中与您的问题相关的训练问题的发现。 Here这是我在互联网上进行了数小时的研究后发现的。