Tensorflow Softmax_cross...() Function Float Type Error
I resolved in issue from this post, and the use Olivier recommended using the softmax_cross_entropy_with_logits() function. He is correct, but I'm getting a weird data type error.
Solution 1:
You have created a placeholder with:
y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes], name="ground_truth")
The error is quite clear:
You must feed a value for placeholder tensor 'ground_truth'
When calling sess.run([cost, accuracy], feed_dict={x: batch_x, temp_y: batch_y, keep_prob: 1.0})
, you are not feeding the parameter y
.
You should instead use:
batch_y = convert_to_2_channel(batch_y, batch_size)
# do not reshape batch_y now
sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y,
keep_prob: 1.0})
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