if` __name__ == '__main__':
aae = AdversarialAutoencoder()
Layer (type) Output Shape Param #
=================================================================
dense_10 (Dense) (None, 512) 51712
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 512) 0
_________________________________________________________________
batch_normalization_7 (Batch (None, 512) 2048
_________________________________________________________________
dense_11 (Dense) (None, 512) 262656
_________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 512) 0
_________________________________________________________________
batch_normalization_8 (Batch (None, 512) 2048
_________________________________________________________________
dense_12 (Dense) (None, 1) 513
=================================================================
Total params: 318,977
Trainable params: 316,929
Non-trainable params: 2,048
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten_2 (Flatten) (None, 784) 0
_________________________________________________________________
dense_13 (Dense) (None, 512) 401920
_________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, 512) 0
_________________________________________________________________
batch_normalization_9 (Batch (None, 512) 2048
_________________________________________________________________
dense_14 (Dense) (None, 512) 262656
_________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, 512) 0
_________________________________________________________________
batch_normalization_10 (Batc (None, 512) 2048
_________________________________________________________________
dense_15 (Dense) (None, 100) 51300
=================================================================
Total params: 719,972
Trainable params: 717,924
Non-trainable params: 2,048
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_16 (Dense) (None, 512) 51712
_________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, 512) 0
_________________________________________________________________
batch_normalization_11 (Batc (None, 512) 2048
_________________________________________________________________
dense_17 (Dense) (None, 512) 262656
_________________________________________________________________
leaky_re_lu_12 (LeakyReLU) (None, 512) 0
_________________________________________________________________
batch_normalization_12 (Batc (None, 512) 2048
_________________________________________________________________
dense_18 (Dense) (None, 784) 402192
_________________________________________________________________
reshape_2 (Reshape) (None, 28, 28, 1) 0
=================================================================
Total params: 720,656
Trainable params: 718,608
Non-trainable params: 2,048
aae.train(epochs=2000, batch_size=32, save_interval=200)
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-6-159ebfef0877> in <module>()
----> 1 aae.train(epochs=2000, batch_size=32, save_interval=200)
<ipython-input-2-57f851a1818e> in train(self, epochs, batch_size, save_interval)
113
114 # Generate a half batch of new images
--> 115 latent_fake, gen_imgs = self.generator.predict(imgs)
116
117 latent_real = np.random.normal(size=(half_batch, self.encoded_dim))
/usr/local/lib/python2.7/dist-packages/Keras-2.0.8-py2.7.egg/keras/engine/training.pyc in predict(self, x, batch_size, verbose, steps)
1715 f = self.predict_function
1716 return self._predict_loop(f, ins, batch_size=batch_size,
-> 1717 verbose=verbose, steps=steps)
1718
1719 def train_on_batch(self, x, y,
/usr/local/lib/python2.7/dist-packages/Keras-2.0.8-py2.7.egg/keras/engine/training.pyc in _predict_loop(self, f, ins, batch_size, verbose, steps)
1267 else:
1268 ins_batch = _slice_arrays(ins, batch_ids)
-> 1269 batch_outs = f(ins_batch)
1270 if not isinstance(batch_outs, list):
1271 batch_outs = [batch_outs]
/usr/local/lib/python2.7/dist-packages/Keras-2.0.8-py2.7.egg/keras/backend/tensorflow_backend.pyc in __call__(self, inputs)
2255 updated = session.run(self.outputs + [self.updates_op],
2256 feed_dict=feed_dict,
-> 2257 **self.session_kwargs)
2258 return updated[:len(self.outputs)]
2259
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
893 try:
894 result = self._run(None, fetches, feed_dict, options_ptr,
--> 895 run_metadata_ptr)
896 if run_metadata:
897 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
1122 if final_fetches or final_targets or (handle and feed_dict_tensor):
1123 results = self._do_run(handle, final_targets, final_fetches,
-> 1124 feed_dict_tensor, options, run_metadata)
1125 else:
1126 results = []
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1319 if handle is None:
1320 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1321 options, run_metadata)
1322 else:
1323 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1338 except KeyError:
1339 pass
-> 1340 raise type(e)(node_def, op, message)
1341
1342 def _extend_graph(self):
InvalidArgumentError: Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero
[[Node: sequential_5/flatten_2/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_input_5_0_1/_213, sequential_5/flatten_2/stack)]]
Caused by op u'sequential_5/flatten_2/Reshape', defined at:
File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python2.7/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/python2.7/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-4-3d96210dd081>", line 2, in <module>
aae = AdversarialAutoencoder()
File "<ipython-input-2-57f851a1818e>", line 18, in __init__
self.generator = self.build_generator()
File "<ipython-input-2-57f851a1818e>", line 54, in build_generator
encoded_repr = encoder(img)
File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 602, in __call__
output = self.call(inputs, **kwargs)
File "build/bdist.linux-x86_64/egg/keras/models.py", line 532, in call
return self.model.call(inputs, mask)
File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 2058, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 2209, in run_internal_graph
output_tensors = _to_list(layer.call(computed_tensor, **kwargs))
File "build/bdist.linux-x86_64/egg/keras/layers/core.py", line 484, in call
return K.batch_flatten(inputs)
File "build/bdist.linux-x86_64/egg/keras/backend/tensorflow_backend.py", line 1918, in batch_flatten
x = tf.reshape(x, tf.stack([-1, prod(shape(x)[1:])]))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 2619, in reshape
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero
[[Node: sequential_5/flatten_2/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_input_5_0_1/_213, sequential_5/flatten_2/stack)]]