Question

我正在尝试将keras 2.0与pymc3结合起来构建一个神经网络。它是Thomas Weicki's Bayesian deep learning II代码的修改这是我的代码：

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))

但是我收到以下错误，我不知道如何修复：

追踪（最近一次呼叫最后一次）：

  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

Answer 1

不幸的是，使用Keras 2.0，您无法再使用符号初始值设定项来表示权重。尝试降级到Keras 1.2它会起作用。

请参阅以下问题以供参考：

https://github.com/fchollet/keras/issues/6546

https://github.com/fchollet/keras/issues/6551

结合Keras 2.0和pymc3的问题

1 个答案: