TypeError: Input 'b' of 'MatMul' Op has type float32 that does not match type int32 of argument 'a'

I've met the same problem using Tensorflow r1.4 with Python 3.4.

Indeed, I think you need to change the code

tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,
                 num_sampled, vocabulary_size))

into

tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,
                 num_sampled, vocabulary_size))

or

loss = tf.reduce_mean(tf.nn.nce_loss(
        weights = softmax_weights,
        biases = softmax_biases, 
        inputs = embed, 
        labels = train_labels, 
        num_sampled = num_sampled, 
        num_classes = vocabulary_size))

Meanwhile, you need to change the code back to

similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))

It wrong to use tf.cast(..., tf.int32) and actually, there is no need to use tf.cast(..., tf.float32) because it's already been tf.float32.

p.s.

The solution is also useful when you meet the problem while using tf.nn.sampled_softmax_loss() because the usage of sampled_softmax_loss() is quite similar to nce_loss().

Why are you doing the matrix multiplication in integer space? You probably want both of those tf.cast to be to tf.float32.