felixmohr / deep-learning-with-python

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

Deep Learning with Python

Here I will put some projects that I create while learning how to apply deep neural networks. Current projects:

• Variational Autoencoder for creating MNIST characters (notebook with explanations)
• Deep Convolutional Generative Adversarial Network (DCGAN) for the same purpose (notebook with explanations)
• DCGAN for creating human faces
• Style Transfer

deep-learning-with-python's Issues

Find bugs and improve the existing code

If you find some code that might need adjustments, feel free to contribute.

Updating the README

InvalidArgumentError: Input to reshape is a tensor with 3200 values, but the requested shape requires a multiple of 49 [[{{node decoder/Reshape}}]]

Error while reshaping the tensor in one of CNN layers.

---

InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1333 try:
-> 1334 return fn(*args)
1335 except errors.OpError as e:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata)
1320

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1406 self._session, options, feed_dict, fetch_list, target_list,
-> 1407 run_metadata)
1408

InvalidArgumentError: Input to reshape is a tensor with 3200 values, but the requested shape requires a multiple of 49
[[{{node decoder/Reshape}}]]

During handling of the above exception, another exception occurred:

InvalidArgumentError Traceback (most recent call last)
in ()
75 for i in range(30000):
76 batch = [np.reshape(b, [28, 28]) for b in mnist.train.next_batch(batch_size=batch_size)[0]]
---> 77 sess.run(optimizer, feed_dict = {X_in: batch, Y: batch, keep_prob: 0.8})
78
79 if not i % 200:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and feed_dict_tensor):
1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata)
1153 else:
1154 results = []

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1326 if handle is None:
1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata)
1329 else:
1330 return self._do_call(_prun_fn, handle, feeds, fetches)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1346 pass
1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message)
1349
1350 def _extend_graph(self):

InvalidArgumentError: Input to reshape is a tensor with 3200 values, but the requested shape requires a multiple of 49
[[node decoder/Reshape (defined at :52) ]]

Caused by op 'decoder/Reshape', defined at:
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py", line 16, in
app.launch_new_instance()
File "/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 65, in
dec = decoder(sampled, keep_prob)
File "", line 52, in decoder
x = tf.reshape(x, reshaped_dim)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 7179, in reshape
"Reshape", tensor=tensor, shape=shape, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1801, in init
self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 3200 values, but the requested shape requires a multiple of 49
[[node decoder/Reshape (defined at :52) ]]

Additional Deep Learning Notebooks or Scripts with Python

Feel free to contribute your own Deep Learning tutorials to this repository.

dst_loss not defined

in VAE.ipynb, dst_loss is not defined. based on your write up, i think you were tracking latent_loss...

How to generate 200*200 pxl face images?

Hi, Iam trying to run youre code on LFW also,I want to generate images with 200*200 pxl..
I change values where were 40 pxl to 200 but Iam getting thiss error :

InvalidArgumentError
Input to reshape is a tensor with 307200 values, but the requested shape requires a multiple of 120000

how is possible to generate 200 pxl pictures? Can you help,where we need to make changes?

How did you choose these values??

reshaped_dim = [-1, 7, 7, dec_in_channels]
inputs_decoder = 49 * dec_in_channels / 2

"Ambiguous dimension: 24.5" in decoder call

I'm trying to run the VAE example by copying and pasting the code into a .py file. I'm getting the error below when I try to run it. Unfortunately I'm a python newbie and don't know what's going on. Any help is appreciated!

In [64]: dec = decoder(sampled, keep_prob)
Traceback (most recent call last):

File "", line 1, in
dec = decoder(sampled, keep_prob)

File "", line 60, in decoder
x = tf.layers.dense(sampled_z, units=inputs_decoder, activation=lrelu)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/layers/core.py", line 215, in dense
return layer.apply(inputs)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 492, in apply
return self.call(inputs, *args, **kwargs)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 434, in call
self.build(input_shapes[0])

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/layers/core.py", line 118, in build
trainable=True)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 374, in add_variable
trainable=trainable and self.trainable)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1065, in get_variable
use_resource=use_resource, custom_getter=custom_getter)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 962, in get_variable
use_resource=use_resource, custom_getter=custom_getter)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 367, in get_variable
validate_shape=validate_shape, use_resource=use_resource)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 352, in _true_getter
use_resource=use_resource)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 653, in _get_single_variable
shape = tensor_shape.as_shape(shape)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 798, in as_shape
return TensorShape(shape)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 434, in init
self._dims = [as_dimension(d) for d in dims_iter]

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 434, in
self._dims = [as_dimension(d) for d in dims_iter]

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 376, in as_dimension
return Dimension(value)

File "/Users/Mercy/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 35, in init
raise ValueError("Ambiguous dimension: %s" % value)

ValueError: Ambiguous dimension: 24.5

Cannot feed value of shape (0,) for Tensor u'X:0', which has shape '(?, 40, 40, 3)'

In the very last block of code in the Faces GAN there seems to be this statement
d_real_ls, d_fake_ls, g_ls, d_ls = sess.run([loss_d_real, loss_d_fake, loss_g, loss_d], feed_dict={X_in: batch, noise: n, keep_prob: keep_prob_train, is_training:True})
Please provide the way to get it running since the rest of the code seems ok.