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Lab: Keras Traffic Sign Classification

Udacity - Self-Driving Car NanoDegree

Combined Image

We've prepared a Jupyter notebook that will guide you through the process of building and training a Traffic Sign Classification network with Keras.

Instructions

  1. Set up your development environment with the CarND Starter Kit
  2. Launch the Jupyter notebook: jupyter notebook traffic-sign-classification-with-keras.ipynb
  3. Follow the instructions in the notebook

Since this is a self-assessed lab, you can view the solution by running the traffic-sign-classification-with-keras-solution.ipynb notebook. Make sure you're only running one notebook at a time.

Help

Remember that you can get assistance from mentors and fellow students in Student Hub or in Knowledge. You can also review the concepts from the previous lessons, or consult external documentation.

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carnd-keras-lab's Issues

Error while running solution: "Reshape cannot infer the missing input size ..."

I got the following error while running the solution workbook:

Train on 31367 samples, validate on 7842 samples
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020     try:
-> 1021       return fn(*args)
   1022     except errors.OpError as e:

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002                                  feed_dict, fetch_list, target_list,
-> 1003                                  status, run_metadata)
   1004 

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
    468           compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 469           pywrap_tensorflow.TF_GetCode(status))
    470   finally:

InvalidArgumentError: Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero
	 [[Node: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_flatten_input_2_0/_145, stack_1)]]
	 [[Node: Mean_27/_167 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_367_Mean_27", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-31-0e034821d2da> in <module>()
      1 # TODO: Compile and train the model here.
      2 model.compile('adam', 'categorical_crossentropy', ['accuracy'])
----> 3 history = model.fit(X_normalized, y_one_hot, nb_epoch=10, validation_split=0.2)

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/models.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)
    662                               shuffle=shuffle,
    663                               class_weight=class_weight,
--> 664                               sample_weight=sample_weight)
    665 
    666     def evaluate(self, x, y, batch_size=32, verbose=1,

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch)
   1141                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1142                               callback_metrics=callback_metrics,
-> 1143                               initial_epoch=initial_epoch)
   1144 
   1145     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
    841                 batch_logs['size'] = len(batch_ids)
    842                 callbacks.on_batch_begin(batch_index, batch_logs)
--> 843                 outs = f(ins_batch)
    844                 if not isinstance(outs, list):
    845                     outs = [outs]

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   1601         session = get_session()
   1602         updated = session.run(self.outputs + [self.updates_op],
-> 1603                               feed_dict=feed_dict)
   1604         return updated[:len(self.outputs)]
   1605 

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    764     try:
    765       result = self._run(None, fetches, feed_dict, options_ptr,
--> 766                          run_metadata_ptr)
    767       if run_metadata:
    768         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    962     if final_fetches or final_targets:
    963       results = self._do_run(handle, final_targets, final_fetches,
--> 964                              feed_dict_string, options, run_metadata)
    965     else:
    966       results = []

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1012     if handle is None:
   1013       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1014                            target_list, options, run_metadata)
   1015     else:
   1016       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1032         except KeyError:
   1033           pass
-> 1034       raise type(e)(node_def, op, message)
   1035 
   1036   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: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_flatten_input_2_0/_145, stack_1)]]
	 [[Node: Mean_27/_167 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_367_Mean_27", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-31-0e034821d2da> in <module>()
      1 # TODO: Compile and train the model here.
      2 model.compile('adam', 'categorical_crossentropy', ['accuracy'])
----> 3 history = model.fit(X_normalized, y_one_hot, nb_epoch=10, validation_split=0.2)

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/models.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)
    662                               shuffle=shuffle,
    663                               class_weight=class_weight,
--> 664                               sample_weight=sample_weight)
    665 
    666     def evaluate(self, x, y, batch_size=32, verbose=1,

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch)
   1141                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1142                               callback_metrics=callback_metrics,
-> 1143                               initial_epoch=initial_epoch)
   1144 
   1145     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
    841                 batch_logs['size'] = len(batch_ids)
    842                 callbacks.on_batch_begin(batch_index, batch_logs)
--> 843                 outs = f(ins_batch)
    844                 if not isinstance(outs, list):
    845                     outs = [outs]

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   1601         session = get_session()
   1602         updated = session.run(self.outputs + [self.updates_op],
-> 1603                               feed_dict=feed_dict)
   1604         return updated[:len(self.outputs)]
   1605 

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    764     try:
    765       result = self._run(None, fetches, feed_dict, options_ptr,
--> 766                          run_metadata_ptr)
    767       if run_metadata:
    768         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    962     if final_fetches or final_targets:
    963       results = self._do_run(handle, final_targets, final_fetches,
--> 964                              feed_dict_string, options, run_metadata)
    965     else:
    966       results = []

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1012     if handle is None:
   1013       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1014                            target_list, options, run_metadata)
   1015     else:
   1016       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1032         except KeyError:
   1033           pass
-> 1034       raise type(e)(node_def, op, message)
   1035 
   1036   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: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_flatten_input_2_0/_145, stack_1)]]
	 [[Node: Mean_27/_167 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_367_Mean_27", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op 'Reshape_1', defined at:
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/runpy.py", line 184, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/kernelapp.py", line 474, in start
    ioloop.IOLoop.instance().start()
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tornado/ioloop.py", line 887, in start
    handler_func(fd_obj, events)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
    user_expressions, allow_stdin)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
    if self.run_code(code, result):
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-24-e6331a6c842a>", line 7, in <module>
    model.add(Flatten(input_shape=(32, 32, 3)))
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/models.py", line 294, in add
    layer.create_input_layer(batch_input_shape, input_dtype)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/topology.py", line 398, in create_input_layer
    self(x)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/topology.py", line 569, in __call__
    self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/topology.py", line 632, in add_inbound_node
    Node.create_node(self, inbound_layers, node_indices, tensor_indices)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/engine/topology.py", line 164, in create_node
    output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/layers/core.py", line 443, in call
    return K.batch_flatten(x)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1353, in batch_flatten
    x = tf.reshape(x, stack([-1, prod(shape(x)[1:])]))
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2448, in reshape
    name=name)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
    op_def=op_def)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/frank/Apps/Anaconda3/envs/CarND-Keras-Lab/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

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: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_flatten_input_2_0/_145, stack_1)]]
	 [[Node: Mean_27/_167 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_367_Mean_27", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Missing batch_size ?

In the solution,

Train the Network

  1. Compile the network using adam optimizer and categorical_crossentropy loss function.
  2. Train the network for ten epochs and validate with 20% of the training data.

history = model.fit(X_normalized, y_one_hot, nb_epoch=10, validation_split=0.2)

Does it missing batch_size = 128 ? I see the batch_size in the similar code in the rest part of the solution.

Or what will be the default value if we don't assign batch size.

Warning in Keras

In the newer version of Keras 2.0.2,

the nb_epoch in

history = model.fit(X_normalized, y_one_hot, batch_size=128, nb_epoch=2, validation_split=0.2, verbose=2)

is changed to epochs

history = model.fit(X_normalized, y_one_hot, batch_size=128, epochs=2, validation_split=0.2, verbose=2)

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