hfawaz / bigdata18 Goto Github PK

x_trainScaledNPArray = np.array(x_trainScaled)
x_testScaledNPArray = np.array(x_testScaled)
y_trainNPArray = np.array(y_train)
y_testNPArray = np.array(y_test)

batch_size = 16
nb_epochs = 1 # 2000
verbose = True
    
pathToModelCheckpoints = 'myPath/'
pathToModel = 'myPathToModel/'
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=50,min_lr=0.0001)
# model checkpoint
model_checkpoint = keras.callbacks.ModelCheckpoint(filepath=pathToModelCheckpoints, monitor='loss',save_best_only=True)
callbacks=[reduce_lr,model_checkpoint]
input_shape = x_trainScaledNPArray.shape
nb_classes = len(np.unique(y_train))
model = TransferLearningMain.build_model(input_shape, nb_classes, pre_model=None)
TransferLearningMain.train(x_trainScaledNPArray,y_trainNPArray,x_testScaledNPArray,y_testNPArray,batch_size,verbose,nb_epochs,callbacks,pathToModelCheckpoints,pre_model=None)
model.save(pathToModel)

print(x_trainScaledNPArray.shape)
print(x_testScaledNPArray.shape)

model = keras.models.load_model(model_path)
y_pred = model.predict(x_test,batch_size=4)

(18287, 2048)
(347, 2048)

Traceback (most recent call last):
 [...]
    y_pred = model.predict(x_test,batch_size=4)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 1727, in predict
    tmp_batch_outputs = self.predict_function(iterator)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py", line 764, in _initialize
    *args, **kwds))
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py", line 3050, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py", line 986, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1569 predict_function  *
        return step_function(self, iterator)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1559 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:1285 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2833 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:3608 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1552 run_step  **
        outputs = model.predict_step(data)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:1525 predict_step
        return self(x, training=False)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:1013 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    C:\Users\myUser\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\input_spec.py:270 assert_input_compatibility
        ', found shape=' + display_shape(x.shape))

    ValueError: Input 0 is incompatible with layer model: expected shape=(None, 18287, 2048), found shape=(None, 2048)

will it work for  multivariate  time series classification for example mixture of categorical and continues data? 
for example at time t1 we have observation: red, 2.4 , 5, 12.456 and time t2: green, 3.5, 2, 45.78; time t3: black, 5.6, 7, 23.56; t4: red, 2.1, 5, 12.6 ?