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Ich versuche Keras 2.0 mit Pymc3 zu kombinieren, um ein neuronales Netzwerk aufzubauen. Es ist eine Modifikation des Codes von Thomas Weicki's Bayesian deep learning II Dies ist der Code ich habe:Probleme bei der Kombination von Keras 2.0 und pymc3
import numpy as np
import pymc3 as pm
import theano
import theano.tensor as T
from keras.layers import Input, Dense
from keras import backend as K
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_moons
from scipy.stats import mode
X, Y = make_moons(noise=0.2, random_state=0, n_samples=1000)
X = scale(X)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.6)
ann_input = theano.shared(X_train.astype(np.float32))
ann_output = theano.shared(Y_train.astype(np.float32))
print (X_train.shape)
print (Y_train.shape)
class GaussWeights(object):
def __init__(self):
self.count = 0
def __call__(self, shape, name='w',dtype=None):
return pm.Normal(
name, mu=0, sd=.1,
testval=K.random_normal(shape,dtype=dtype),
shape=shape)
n_hidden = 16
def build_ann(x, y, init):
b = (T.ones_like(x[:]))
rows = b.shape.eval()[0]
cols = b.shape.eval()[1]
with pm.Model() as m:
i = Input(tensor=x, shape=(rows,cols))
layer1 = Dense(16,kernel_initializer=init, activation='tanh')(i)
layer2 = Dense(1, kernel_initializer=init, activation='sigmoid')(layer1)
layer2 = layer2.reshape((rows,))
out = pm.Bernoulli('out', layer2, observed=y)
return m, out
#m,out = build_ann(ann_input, ann_output)
m,out = build_ann(ann_input, ann_output, GaussWeights())
with m:
#Run ADVI which returns posterior means, standard deviations, and the evidence lower bound (ELBO)
ann_input.set_value(X_train.astype(np.float32))
ann_output.set_value(Y_train.astype(np.float32))
v_params = pm.variational.advi(n=50000)
trace = pm.variational.sample_vp(v_params, draws=5000)
# Replace shared variables with testing set
ann_input.set_value(X_test.astype(np.float32))
ann_output.set_value(Y_test.astype(np.float32))
with m:
ppc = pm.sample_ppc(trace, samples=500)
# Use probability of > 0.5 to assume prediction of class 1
pred = ppc['out'].mean(axis=0) > 0.5
pred_mode = mode(ppc['out'], axis=0).mode[0, :]
print (pred.shape)
print('Accuracy = {}%'.format((Y_test == pred).mean() * 100))
Aber ich bekomme die folgende Fehlermeldung, die ich nicht weiß, wie sie zu beheben:
Traceback (jüngste rufen Sie zuletzt):
File "keras_deep_learning.py", line 50, in <module>
m,out = build_ann(ann_input, ann_output, GaussWeights())
File "keras_deep_learning.py", line 43, in build_ann
layer1 = Dense(16,kernel_initializer=init, activation='tanh')(i)
File "/home/gbenga/.local/lib/python3.5/site-packages/keras/engine/topology.py", line 558, in __call__
self.build(input_shapes[0])
File "/home/gbenga/.local/lib/python3.5/site-packages/keras/layers/core.py", line 827, in build
constraint=self.kernel_constraint)
File "/home/gbenga/.local/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "/home/gbenga/.local/lib/python3.5/site-packages/keras/engine/topology.py", line 391, in add_weight
weight = K.variable(initializer(shape), dtype=dtype, name=name)
File "/home/gbenga/.local/lib/python3.5/site-packages/keras/backend/theano_backend.py", line 143, in variable
value = value.eval()
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/gof/graph.py", line 516, in eval
self._fn_cache[inputs] = theano.function(inputs, self)
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/compile/function.py", line 326, in function
output_keys=output_keys)
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/compile/pfunc.py", line 486, in pfunc
output_keys=output_keys)
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/compile/function_module.py", line 1794, in orig_function
output_keys=output_keys).create(
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/compile/function_module.py", line 1446, in __init__
accept_inplace)
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/compile/function_module.py", line 177, in std_fgraph
update_mapping=update_mapping)
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/gof/fg.py", line 180, in __init__
self.__import_r__(output, reason="init")
File "/home/gbenga/.local/lib/python3.5/site-packages/theano/gof/fg.py", line 361, in __import_r__
raise MissingInputError("Undeclared input", variable=variable)
theano.gof.fg.MissingInputError: Undeclared input