tensorflow-for- cost cost分类，成本始终为0

Question

这是从这篇文章(不是我的)开始的：TensorFlow for binary classification

我遇到了类似的问题，并将我的数据转换为使用一种热编码。然而，我得到的成本仍然是0。有趣的是，当我将训练数据反馈给它时，准确率是正确的(90%)。

代码如下：

# Set parameters
learning_rate = 0.02
training_iteration = 2
batch_size = int(np.size(y_vals)/300)
display_step = 1
numOfFeatures = 20 # 784 if MNIST
numOfClasses = 2 #10 if MNIST dataset

# TF graph input
x = tf.placeholder("float", [None, numOfFeatures]) 
y = tf.placeholder("float", [None, numOfClasses]) 

# Create a model

# Set model weights to random numbers: https://www.tensorflow.org/api_docs/python/tf/random_normal
W = tf.Variable(tf.random_normal(shape=[numOfFeatures,1]))  # Weight vector
b = tf.Variable(tf.random_normal(shape=[1,1]))              # Constant

# Construct a linear model
model = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Minimize error using cross entropy
# Cross entropy
cost_function = -tf.reduce_sum(y*tf.log(model)) 
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for iteration in range(training_iteration):
        avg_cost = 0.

        total_batch = int(len(x_vals)/batch_size)
        # Loop over all batches
        for i in range(total_batch):

            batch_xs = x_vals[i*batch_size:(i*batch_size)+batch_size]
            batch_ys = y_vals_onehot[i*batch_size:(i*batch_size)+batch_size]

            # Fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})

            # Compute average loss
            avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch

        # Display logs per eiteration step
        if iteration % display_step == 0:
            print ("Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost))

    print ("Tuning completed!")

    # Evaluation function
    correct_prediction = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))   
    #correct_prediction = tf.equal(model, y)   
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    # Test the model
    print ("Accuracy:", accuracy.eval({x: x_vals_test, y: y_vals_test_onehot}))

Answer 1

您对成本的输出是使用：

"{:.9f}".format(avg_cost)

因此，也许你可以用更大的数字来代替9。

Answer 2

好了，这是我在最后发现的。

替换：

b = tf.Variable(tf.random_normal(shape=[1,1]))

通过以下方式：

b = tf.Variable(tf.zeros([1]))