hiranumn / integratedgradients Goto Github PK

python
**MissingInputError                         Traceback (most recent call last)
<ipython-input-33-26950f8e5048> in <module>()
----> 1 plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')

<ipython-input-32-5d76776930b0> in plot_roc(v_teste, c_teste, t_conservation, model_file)
    109 
    110 
--> 111     integrated_gradients(model=merged_model, outchannels=[0])

/home/lucas/PycharmProjects/MEGS_introns/deeplearning/IntegratedGradients.py in __init__(self, model, outchannels, verbose)
     77 
     78             # Build computational graph that calculates the tensfor given inputs
---> 79             self.get_gradients[c] = K.function(inputs=self.input_tensors, outputs=gradients)
     80 
     81             # This takes a lot of time for a big model with many tasks.

/home/lucas/miniconda2/lib/python2.7/site-packages/keras/backend/theano_backend.pyc in function(inputs, outputs, updates, **kwargs)
    680                 msg = "Invalid argument '%s' passed to K.function" % key
    681                 raise ValueError(msg)
--> 682     return Function(inputs, outputs, updates=updates, **kwargs)
    683 
    684 

/home/lucas/miniconda2/lib/python2.7/site-packages/keras/backend/theano_backend.pyc in __init__(self, inputs, outputs, updates, **kwargs)
    666                                         allow_input_downcast=True,
    667                                         on_unused_input='ignore',
--> 668                                         **kwargs)
    669 
    670     def __call__(self, inputs):

/home/lucas/.local/lib/python2.7/site-packages/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

/home/lucas/.local/lib/python2.7/site-packages/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)
    484                          accept_inplace=accept_inplace, name=name,
    485                          profile=profile, on_unused_input=on_unused_input,
--> 486                          output_keys=output_keys)
    487 
    488 

/home/lucas/.local/lib/python2.7/site-packages/theano/compile/function_module.pyc in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)
   1792                    profile=profile,
   1793                    on_unused_input=on_unused_input,
-> 1794                    output_keys=output_keys).create(
   1795             defaults)
   1796 

/home/lucas/.local/lib/python2.7/site-packages/theano/compile/function_module.pyc in __init__(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys)
   1444             # OUTPUT VARIABLES)
   1445             fgraph, additional_outputs = std_fgraph(inputs, outputs,
-> 1446                                                     accept_inplace)
   1447             fgraph.profile = profile
   1448         else:

/home/lucas/.local/lib/python2.7/site-packages/theano/compile/function_module.pyc in std_fgraph(input_specs, output_specs, accept_inplace)
    175 
    176     fgraph = gof.fg.FunctionGraph(orig_inputs, orig_outputs,
--> 177                                   update_mapping=update_mapping)
    178 
    179     for node in fgraph.apply_nodes:

/home/lucas/.local/lib/python2.7/site-packages/theano/gof/fg.pyc in __init__(self, inputs, outputs, features, clone, update_mapping)
    178 
    179         for output in outputs:
--> 180             self.__import_r__(output, reason="init")
    181         for i, output in enumerate(outputs):
    182             output.clients.append(('output', i))

/home/lucas/.local/lib/python2.7/site-packages/theano/gof/fg.pyc in __import_r__(self, variable, reason)
    349         # Imports the owners of the variables
    350         if variable.owner and variable.owner not in self.apply_nodes:
--> 351                 self.__import__(variable.owner, reason=reason)
    352         elif (variable.owner is None and
    353                 not isinstance(variable, graph.Constant) and

/home/lucas/.local/lib/python2.7/site-packages/theano/gof/fg.pyc in __import__(self, apply_node, check, reason)
    395                                      % (node.inputs.index(r), str(node)))
    396                         error_msg += get_variable_trace_string(r)
--> 397                         raise MissingInputError(error_msg, variable=r)
    398 
    399         for node in new_nodes:

MissingInputError: Input 0 of the graph (indices start from 0), used to compute InplaceDimShuffle{0,1,2,x}(convolution1d_input_26), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error. 
Backtrace when that variable is created:

  File "/home/lucas/miniconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-33-26950f8e5048>", line 1, in <module>
    plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')
  File "<ipython-input-32-5d76776930b0>", line 16, in plot_roc
    model_up_conservation.add(Convolution1D(100, filter_first_layer_size, input_shape=(SIZE_SEQUENCE, 1), name='up_conservation_intron_layer'))
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/models.py", line 276, in add
    layer.create_input_layer(batch_input_shape, input_dtype)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 367, in create_input_layer
    dtype=input_dtype, name=name)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 1085, in Input
    input_tensor=tensor)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 1008, in __init__
    name=self.name)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/backend/theano_backend.py", line 54, in placeholder
    x = T.TensorType(dtype, broadcast)(name)


Backtrace when the variable is created:
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-33-26950f8e5048>", line 1, in <module>
    plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')
  File "<ipython-input-32-5d76776930b0>", line 16, in plot_roc
    model_up_conservation.add(Convolution1D(100, filter_first_layer_size, input_shape=(SIZE_SEQUENCE, 1), name='up_conservation_intron_layer'))
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/models.py", line 276, in add
    layer.create_input_layer(batch_input_shape, input_dtype)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 367, in create_input_layer
    dtype=input_dtype, name=name)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 1085, in Input
    input_tensor=tensor)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 1008, in __init__
    name=self.name)
  File "/home/lucas/miniconda2/lib/python2.7/site-packages/keras/backend/theano_backend.py", line 54, in placeholder
    x = T.TensorType(dtype, broadcast)(name)**

TypeError                                 Traceback (most recent call last)
<ipython-input-15-73a1b345b022> in <module>()
----> 1 ig = integrated_gradients(model)

/home/ubuntu/GA/IntegratedGradients-master/IntegratedGradients.py in __init__(self, model, outchannels, verbose)
     73                 gradients = self.model.optimizer.get_gradients(self.model.output[:, c], self.model.input)
     74             if K.backend() == "theano":
---> 75                 gradients = self.model.optimizer.get_gradients(self.model.output[:, c].sum(), self.model.input)
     76 
     77             # Build computational graph that computes the tensors given inputs

/home/ubuntu/anaconda3/envs/theano_p27/lib/python2.7/site-packages/keras/optimizers.pyc in get_gradients(self, loss, params)
     88     def get_gradients(self, loss, params):
     89         grads = K.gradients(loss, params)
---> 90         if None in grads:
     91             raise ValueError('An operation has `None` for gradient. '
     92                              'Please make sure that all of your ops have a '

/home/ubuntu/anaconda3/envs/theano_p27/lib/python2.7/site-packages/theano/tensor/var.pyc in __iter__(self)
    630         except TypeError:
    631             # This prevents accidental iteration via builtin.sum(self)
--> 632             raise TypeError(('TensorType does not support iteration. '
    633                              'Maybe you are using builtin.sum instead of '
    634                              'theano.tensor.sum? (Maybe .max?)'))

TypeError: TensorType does not support iteration. Maybe you are using builtin.sum instead of theano.tensor.sum? (Maybe .max?)

Traceback (most recent call last):
  File "avocado_fit.py", line 66, in <module>
    ig = integrated_gradients(model)
  File "/net/noble/vol1/home/jmschr/proj/avocado/exps/2_13_18_Intepretability/IntegratedGradients.py", line 57, in __init__
    self.outchannels = range(model1.output._keras_shape[1])
NameError: global name 'model1' is not defined

if len(self.outchannels) == 0:
            if verbose: print("Evaluated output channel (0-based index): All")
            if K.backend() == "tensorflow":
                self.outchannels = range(self.model.output.shape[1]._value)
            elif K.backend() == "theano":
                self.outchannels = range(model1.output._keras_shape[1])

AttributeError                            Traceback (most recent call last)
<ipython-input-21-73a1b345b022> in <module>()
----> 1 ig = integrated_gradients(model)

/content/IntegratedGradients/IntegratedGradients.py in __init__(self, model, outchannels, verbose)
     33         #load model supports keras.Model and keras.Sequential
     34         if isinstance(model, Sequential):
---> 35             self.model = model.model
     36         elif isinstance(model, Model):
     37             self.model = model

AttributeError: 'Sequential' object has no attribute 'model'

filter_first_layer_size = 6

model_up = Sequential()
model_up.add(Convolution1D(20, filter_first_layer_size, input_shape=(SIZE_SEQUENCE, 4), name='up_intron_layer'))
model_up.add(Activation('relu'))
#model_up.add(MaxPooling1D(3))
model_up.add(Dropout(0.5))
model_up.add(Convolution1D(100, 3))
model_up.add(Activation('relu'))
model_up.add(MaxPooling1D(2))
#model_up.add(Dropout(0.1))

model_up_conservation = Sequential()
model_up_conservation.add(Convolution1D(100, filter_first_layer_size, input_shape=(SIZE_SEQUENCE, 1), name='up_conservation_intron_layer'))
model_up_conservation.add(Activation('relu'))



merged_conservation_up = Sequential()
merged_conservation_up.add(Merge([model_up, model_up_conservation], mode='concat', concat_axis=1))


model_down = Sequential()
model_down.add(Convolution1D(20, filter_first_layer_size, input_shape=(SIZE_SEQUENCE, 4), name='down_intron_layer'))
model_down.add(Activation('relu'))
#model_down.add(MaxPooling1D(3))
model_down.add(Dropout(0.5))
model_down.add(Convolution1D(100, 3))
model_down.add(Activation('relu'))
model_down.add(MaxPooling1D(2))
#model_down.add(Dropout(0.1))


model_down_conservation = Sequential()
model_down_conservation.add(Convolution1D(100, filter_first_layer_size, input_shape=(SIZE_SEQUENCE, 1), name='down_conservation_intron_layer'))
model_down_conservation.add(Activation('relu'))


merged_conservation_down = Sequential()
merged_conservation_down.add(Merge([model_down, model_down_conservation], mode='concat', concat_axis=1))



merged_model = Sequential()
merged_model.add(Merge([merged_conservation_up, merged_conservation_down], mode='concat', concat_axis=1))
#print merged_model.summary()
#model.add(MaxPooling2D(pool_size=(2, 2)))

merged_model.add(Flatten())
#model.add(Dense(12, input_dim=[4, 200], init='uniform', activation='relu'))
#model.add(Dense(8, init='uniform', activation='relu'))
#model.add(Dense(1, init='uniform', activation='sigmoid'))
merged_model.add(Dense(100))
merged_model.add(Activation('relu'))
merged_model.add(Dropout(0.5))
merged_model.add(Dense(100))
merged_model.add(Activation('relu'))
#merged_model.add(Dropout(0.75))
merged_model.add(Dense(output_dim=1))
merged_model.add(Activation('sigmoid'))


conser_up_teste = np.array([v for v_u in t_conservation for v in v_u[0]])
print conser_up_teste.shape
conser_up_teste = conser_up_teste.reshape(len(conser_up_teste)/SIZE_SEQUENCE, SIZE_SEQUENCE, 1)
print 'reshaped', conser_up_teste.shape
conser_down_teste = np.array([v for v_u in t_conservation for v in v_u[1]])
print conser_down_teste.shape
conser_down_teste = conser_down_teste.reshape(len(conser_down_teste)/SIZE_SEQUENCE, SIZE_SEQUENCE, 1)
print 'reshaped', conser_down_teste.shape
print conser_down_teste[0].shape
print 'saindo'
print np.array([v_down[SIZE_SEQUENCE:] for v_down in v_teste]).shape

merged_model.load_weights(model_file)
merged_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])



scores = merged_model.evaluate(
        [
            np.array([v_up[:SIZE_SEQUENCE] for v_up in v_teste]),  conser_up_teste,  np.array([v_down[SIZE_SEQUENCE:] for v_down in v_teste]), conser_down_teste],


            np.array([c_ts for c_ts in c_teste]),

        verbose=0)
print("%s: %.2f%%" % (merged_model.metrics_names[1], scores[1] * 100))
print('Predicting on test data')

y_score = merged_model.predict(
        [
            np.array([v_up[:SIZE_SEQUENCE] for v_up in v_teste]),
            conser_up_teste,
            np.array([v_down[SIZE_SEQUENCE:] for v_down in v_teste]),
            conser_down_teste
        ])
    


#y_score = model.predict(X_test)

print('Generating results')
generate_results(np.array([c_ts for c_ts in c_teste]),
                y_score[:, 0])



integrated_gradients(model=merged_model)

 #Build a computational graph that computes the tensors given inputs
self.get_gradients[c] = K.function(inputs=self.input_tensors, outputs=gradients)

ValueError                                Traceback (most recent call last)
<ipython-input-8-223e71b35bf0> in <module>()
----> 1 ig = integrated_gradients(model)
      2 exp = ig.explain([X_val[0][1], X_val[1][1], X_val[2][1], X_val[3][1]] )

./IntegratedGradients/IntegratedGradients.py in __init__(self, model, outchannels, verbose)
     74             # Get tensor that calculates gradient
     75             if K.backend() == "tensorflow":
---> 76                 gradients = self.model.optimizer.get_gradients(self.model.output[:, c], self.model.input)
     77             if K.backend() == "theano":
     78                 gradients = self.model.optimizer.get_gradients(self.model.output[:, c].sum(), self.model.input)

~/.local/lib/python3.6/site-packages/keras/optimizers.py in get_gradients(self, loss, params)
     78         grads = K.gradients(loss, params)
     79         if None in grads:
---> 80             raise ValueError('An operation has `None` for gradient. '
     81                              'Please make sure that all of your ops have a '
     82                              'gradient defined (i.e. are differentiable). '

ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.