Question

我是Tensorflow中的新手，并尝试在python 3.6中做MNIST示例。

我建立了代码，以查看错误在＃次迭代中的变化，但是在feed_dict内的sess.run上却给我一个错误。

下面是我的代码，

import tensorflow as tf
import numpy as np
import functools
import sys
sys.path.append('./utils')

from mnist import MNIST

def lazy_property(function):
    attribute = '_cache_' + function.__name__

@property
@functools.wraps(function)
def decorator(self):
    if not hasattr(self, attribute):
        setattr(self, attribute, function(self))
        return getattr(self, attribute)

    return decorator

class Model:

  def __init__(self, image, label):
    self.image = image
    self.label = label
    self.logits
    self.prediction
    self.optimize
    self.error


  @lazy_property
  def logits(self):
    weight = tf.Variable(tf.zeros([img_size_flat, num_classes]))
    #print (img_size_flat)
    biases = tf.Variable(tf.zeros([num_classes]))
    #print (num_classes)
    equation = tf.matmul(self.image, weight) + biases
    return equation

  @lazy_property
  def prediction(self):
    return tf.nn.softmax(self.logits)

  @lazy_property
  def optimize(self):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits, labels=self.label)
    cost = tf.reduce_mean(cross_entropy)
    return tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(cost)


  @lazy_property
  def error(self):
    y_pred_cls = tf.argmax(self.prediction, axis=1)
    mistakes = tf.not_equal(y_true_cls, y_pred_cls)
    #print(mistakes)
    error_from_acc = tf.reduce_mean(tf.cast(mistakes, tf.float32))
    return error_from_acc


batch_size = 100
num_steps = 1000

tf.reset_default_graph()

data = MNIST(data_dir="data/MNIST/")
img_size_flat = data.img_size_flat
img_shape = data.img_shape
num_classes = data.num_classes
image = tf.placeholder(tf.float32, [None, img_size_flat])
label = tf.placeholder(tf.float32, [None, num_classes])
y_true_cls = tf.placeholder(tf.int64, [None])
#print (y_true_cls)
model = Model(image, label)


with tf.Session() as session:
    session.run(tf.global_variables_initializer())

    for step in range(num_steps):
        error = session.run(model.error, {x: data.x_test, y_true: data.y_test}) # Gives me an error message from HERE!!!!
        x_batch, y_true_batch, _ = data.random_batch(batch_size=batch_size)
        session.run(model.optimize, {x: x_batch, y_true: y_true_batch})
        if (step % 100 == 0):
            print("Error rate @ iter %d : %f" % (step, error))

我做错了什么？

我应该将feed_dict（{x: data.x_test, y_true: data.y_test}和{x: x_batch, y_true: y_true_batch}）放在哪里？

还有，我在代码中做错了什么地方吗？

请在这里帮助我。

谢谢。

Answer 1

您已经按照以下方式定义了占位符：

image = tf.placeholder(tf.float32, [None, img_size_flat])
label = tf.placeholder(tf.float32, [None, num_classes])

，但是随后您将x和y_true作为占位符传递到session.run中：

session.run(model.error, {x: data.x_test, y_true: data.y_test})

因此，您需要将x和y_true替换为image和label，并且应该没事：

session.run(model.error, {image : data.x_test, label : data.y_test})

无法将feed_dict键解释为张量以进行批处理和测试

1 个答案: