Issue with multiple input

[LucasSilvaFerreira]

HI,
I'm still receiving error output
(enclosed error log and model)

Thank you

---

MissingInputError Traceback (most recent call last)
in ()
----> 1 plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')

in plot_roc(v_teste, c_teste, t_conservation, model_file)
109
110
--> 111 integrated_gradients(model=merged_model)

/home/lucas/PycharmProjects/MEGS_introns/deeplearning/IntegratedGradients.py in init(self, model, outchannels, verbose)
77
78 # This takes a lot of time for a big model with many tasks.
---> 79 # So lets pring the progress.
80 if verbose:
81 sys.stdout.write('\r')

/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_34), 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 "", line 1, in
plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')
File "", 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 "", line 1, in
plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')
File "", 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)

`

`python
def plot_roc( v_teste,c_teste, t_conservation, model_file):

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)

`

[hiranumn]

Does it work with tensorflow backend?

[LucasSilvaFerreira]

Hi Thanks for helping.
I change the backend and received this error:

AttributeError Traceback (most recent call last)
in ()
----> 1 plot_roc( t_valores, t_grupos, t_conservation, 'model_used_to_paper.hdf5')

in plot_roc(v_teste, c_teste, t_conservation, model_file)
109
110
--> 111 integrated_gradients(model=merged_model)

/home/lucas/PycharmProjects/MEGS_introns/deeplearning/IntegratedGradients.py in init(self, model, outchannels, verbose)
55 elif K.backend() == "theano":
56 self.outchannels = range(model1.output._keras_shape[1])
---> 57 else:
58 if verbose:
59 print "Evaluated output channels (0-based index):",

AttributeError: 'Tensor' object has no attribute 'shape'

[hiranumn]

I cannot seem to reproduce the error. Did anyone else encounter this?