How to implement tf.nn.sigmoid_cross_entropy_with_logits - tensorflow

Question

I am currently learning tensorflow, and I have run into an issue with
tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=logits). The function description says that both labels and logits must be of the same type. I have the function below that I am using to classify MNIST images. The following are key section of my code
X=tf.placeholder(tf.float32,shape=(None,n_inputs),name="X")
y=tf.placeholder(tf.int32,shape=(None),name="y")
def neuron_layer(X,W,b,n_neurons,name,activation=None):
with tf.name_scope(name):
n_inputs=int(X.get_shape()[1])
stddev=2/np.sqrt(n_inputs)
z=tf.matmul(X,W)+b
if activation=="sigmoid":
return tf.math.sigmoid(z)
else:
return z
with tf.name_scope("dnn"):
hidden1=neuron_layer(X,W1,b1,n_hidden1,"hidden",activation="sigmoid")
logits=neuron_layer(hidden1,W2,b2,n_outputs,"outputs",activation="sigmoid")
with tf.name_scope("loss"):
xentropy=tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=logits)
loss=tf.reduce_mean(xentropy,name="loss")
I get the error: input 'y' of 'Mul' Op has type int32 that does not match type float32 of argument 'x
if I change
y=tf.placeholder(tf.float32,shape=(None),name="y"). I get the error
Value passed to parameter 'targets' has DataType float32 not in list of allowed values: int32, int64. Yet logits can only be float32 or float64. Please help me fix the issue. Thanks

Answer 1

As mentioned in the comments, tf.nn.sigmoid_cross_entropy_with_logits is the wrong function. In your case you should use tf.nn.softmax_cross_entropy_with_logits instead (actually, that one yields a deprecation warning, so tf.nn.softmax_cross_entropy_with_logits_v2 is the correct one). Also note, as also mentioned in the comments, that the point of these two functions is that they have a sigmoid (or softmax, respectively) built in, so your model shouldn't have any activation function on the last layer.
Regarding the issue: I just tried it with tensorflow version 1.14.0. There, the issue still occurs if y has type int32. However, it works smoothly if both, y and labels, have type float32.
It's kind of inconsistent that tf.nn.sigmoid_cross_entropy_with_logits does not perform this cast itself, while tf.nn.softmax_cross_entropy_with_logits has no issue with y being int32.