我的神经网络模型有什么问题？

Question

I got a dataset of 178 elements, and each contains 13 features and 1 label. Label is stored as one-hot array. My training dataset is made of 158 elements.

Here is what my model looks like :

x = tf.placeholder(tf.float32, [None,training_data.shape[1]])
y_ = tf.placeholder(tf.float32, [None,training_data_labels.shape[1]])

node_1 = 300
node_2 = 300
node_3 = 300
out_n = 3   

#1
W1 = tf.Variable(tf.random_normal([training_data.shape[1], node_1]))
B1 = tf.Variable(tf.random_normal([node_1]))
y1 = tf.add(tf.matmul(x,W1),B1)
y1 = tf.nn.relu(y1)

#2
W2 = tf.Variable(tf.random_normal([node_1, node_2]))
B2 = tf.Variable(tf.random_normal([node_2]))
y2 = tf.add(tf.matmul(y1,W2),B2)
y2 = tf.nn.relu(y2)

#3
W3 = tf.Variable(tf.random_normal([node_2, node_3]))
B3 = tf.Variable(tf.random_normal([node_3]))
y3 = tf.add(tf.matmul(y2,W3),B3)
y3 = tf.nn.relu(y3)

#output
W4 = tf.Variable(tf.random_normal([node_3, out_n]))
B4 = tf.Variable(tf.random_normal([out_n]))
y4 = tf.add(tf.matmul(y3,W4),B4)
y = tf.nn.softmax(y4)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(200):
        sess.run(optimizer,feed_dict={x:training_data, y_:training_data_labels})

    correct = tf.equal(tf.argmax(y_, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
    print('Accuracy:',accuracy.eval({x:eval_data, y_:eval_data_labels}))

But the accuracy is very low, i tried increase the range 200 to some higher number but it still remains low.

What could I do to improve the results ?

Answer 1

The problem is that you're taking the softmax of y4 and then passing that to tf.nn.softmax_cross_entropy_with_logits . This error is common enough that there's actually a note about it in the documentation for softmax_cross_entropy_with_logits :

WARNING: This op expects unscaled logits, since it performs a softmax on logits internally 
for efficiency. Do not call this op with the output of softmax, as it will produce 
incorrect results.

The rest of your code looks fine, so just replace y4 with y and get rid of y = tf.nn.softmax(y4) .