I'm trying to build a tensorflow neural network using a sigmoid activation hidden layer and a softmax output layer with 3 classes. The outputs are mostly very bad and I believe it is because I am making a mistake in my model construction because I've built a similar model with Matlab and the results have been good. The data is normalized. These results look like this:
[9.2164397e-01 1.6932052e-03 7.6662831e-02]
[3.4100169e-01 2.2419590e-01 4.3480241e-01]
[2.3466848e-06 1.3276369e-04 9.9986482e-01]
[6.5199631e-01 3.4800139e-01 2.3596617e-06]
[9.9879754e-01 9.0103465e-05 1.1123115e-03]
[6.5749985e-01 2.8860433e-02 3.1363973e-01]
My nn looks like this:
def multilayer_perceptron(x, weights, biases, keep_prob):
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.sigmoid(layer_1)
layer_1 = tf.nn.dropout(layer_1, keep_prob)
out_layer = tf.nn.softmax(tf.add(tf.matmul(layer_1,weights['out']),biases['out']))
return out_layer
With the following cost function:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=predictions, labels=y))
I'm growing convinced that my implementation is incorrect and I am doing something very silly. Hours on google and looking at other examples hasn't helped.
UPDATE: When I changed the cost function (shown below), I get decent results. This feels wrong though.
cost = tf.losses.mean_squared_error(predictions=predictions, labels=y)
Your cost function implements a softmax atop of your model output which also has a softmax. You should remove the one in the loss function. Besides this your code seems fine: Are you sure: That the topology (dropout rate, number of layers number of neurons per layer) are the same with both of your models? Are you sure you didn't swar the order of your classes. What about loss and validation loss metric after both trainings?
来源：https://stackoverflow.com/questions/49320188/neural-network-using-softmax-with-strange-outputs