Open nandini123456 opened 6 years ago
oh. If you want to know the probability of each values, I suggest you to make another variable in model() function: `` prob = tf.nn.softmax(Z) prob = tf.reduce_mean(prob, axis = 0, keepdims = True) init = tf.global_variables_initializer()
with tf.Session() as sess: sess.run(init) for epoch in range(epoches): for batch in mini_batches:
# This is the place you put the code for prob:
real_prob = sess.run(prob, feed_dict = {X:X_batch, Y: Y_batch)
print(real_prob) # print to see the probability
Thank you
I trying to add the drop out layer to each convolution layer, but i didn't know how to add that in forward propagation function. can you please help me out.
Sure. Let me clone this project to my current host and I will try to rewrite this code. Wait me until tomorrow. I sure will have the edited part for you.
Thank you
On Wed, Aug 8, 2018 at 10:10 AM, tpvt99 notifications@github.com wrote:
Sure. Let me clone this project to my current host and I will try to rewrite this code. Wait me until tomorrow. I sure will have the edited part for you.
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Hey. Do you mean that add drop out in convolutional layer? Dropout should be added in fully connected layers instead of convolutional layer? Please check again and send me some comments.
I have 36 classes 0-9 and A-Z.I need know the probability value of each classes during testing the trained model