innovation-cat/deeplearningbook

This repository is used to publish source code of my deep learning book

deeplearningbook's Issues

TypeError: ('An update must have the same type as the original shared variable

train = theano.function(
inputs = [index],
outputs = cost,
updates=[(w, w - 0.1 * gw), (b, b - 0.1 * gb)],
givens = {x : train_X[index * batch_size : (index + 1) * batch_size],
y : train_Y[index * batch_size : (index + 1) * batch_size]})

---

TypeError Traceback (most recent call last)
in ()
4 updates=[(w, w - 0.1 * gw), (b, b - 0.1 * gb)],
5 givens = {x : train_X[index * batch_size : (index + 1) * batch_size],
----> 6 y : train_Y[index * batch_size : (index + 1) * batch_size]})

/usr/local/lib/python2.7/dist-packages/Theano-0.10.0.dev1-py2.7.egg/theano/compile/function.pyc in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
324 on_unused_input=on_unused_input,
325 profile=profile,
--> 326 output_keys=output_keys)
327 # We need to add the flag check_aliased inputs if we have any mutable or
328 # borrowed used defined inputs

/usr/local/lib/python2.7/dist-packages/Theano-0.10.0.dev1-py2.7.egg/theano/compile/pfunc.pyc in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
447 rebuild_strict=rebuild_strict,
448 copy_inputs_over=True,
--> 449 no_default_updates=no_default_updates)
450 # extracting the arguments
451 input_variables, cloned_extended_outputs, other_stuff = output_vars

/usr/local/lib/python2.7/dist-packages/Theano-0.10.0.dev1-py2.7.egg/theano/compile/pfunc.pyc in rebuild_collect_shared(outputs, inputs, replace, updates, rebuild_strict, copy_inputs_over, no_default_updates)
206 ' function to remove broadcastable dimensions.')
207
--> 208 raise TypeError(err_msg, err_sug)
209 assert update_val.type == store_into.type
210

TypeError: ('An update must have the same type as the original shared variable (shared_var=b, shared_var.type=TensorType(int64, scalar), update_val=Elemwise{sub,no_inplace}.0, update_val.type=TensorType(float64, scalar)).', 'If the difference is related to the broadcast pattern, you can call the tensor.unbroadcast(var, axis_to_unbroadcast[, ...]) function to remove broadcastable dimensions.')

run out of memory error

I tried the sample cifar10 classification cnn.py. After epoch 0 end. Windows pop up run out of memory error by python program.
My configuration is Windows 10(4 cores/32 GB Ram) anaconda2 installed which meet the HW/SW requirement.

Thanks.