python - 深度网络产生零精度

Question

我正在尝试使用 theano 构建一个深度网络。然而准确度为零。我无法弄清楚我的错误。我正在尝试创建一个具有 3 个隐藏层和一个输出的深度学习网络。我正在努力做一个分类任务，我有 5 个类别。因此，输出层有5个节点。

有什么建议吗？

#!/usr/bin/env python

from __future__ import print_function

import theano
import theano.tensor as T
import lasagne
import numpy as np
import sklearn.datasets
import os
import csv
import pandas as pd

# Lasagne is pre-release, so it's interface is changing.
# Whenever there's a backwards-incompatible change, a warning is raised.
# Let's ignore these for the course of the tutorial
import warnings
warnings.filterwarnings('ignore', module='lasagne')

from lasagne.objectives import categorical_crossentropy, aggregate

#load the data and prepare it
df = pd.read_excel('risk_sample_data_9.20.16_anon.xls',skiprows=0)
rawdata = df.values
# remove empty rows (odd rows)
mask = np.ones(len(rawdata), dtype=bool)
mask[::2] = False
data = rawdata[mask]

idx = np.array([1,5,6,7])
m = np.zeros_like(data)
m[:,idx] = 1
X = np.ma.masked_array(data,m)
X = np.ma.filled(X, fill_value=0)

X = X.astype(theano.config.floatX)
y = data[:,7] # extract financial rating labels
# convert char lables into int , A=1 , B=2, C=3, D=4, F=5
y[y == 'A'] = 1
y[y == 'B'] = 2
y[y == 'C'] = 3
y[y == 'D'] = 4
y[y == 'F'] = 5
y = pd.to_numeric(y)
y = y.astype('int32')
#y = y.astype(theano.config.floatX)
N_CLASSES = 5

# First, construct an input layer.
# The shape parameter defines the expected input shape,
# which is just the shape of our data matrix data.
l_in = lasagne.layers.InputLayer(shape=X.shape)

# We'll create a network with two dense layers:
# A tanh hidden layer and a softmax output layer.
l_hidden1 = lasagne.layers.DenseLayer(
# The first argument is the input layer
l_in,
# This defines the layer's output dimensionality
num_units=250,
# Various nonlinearities are available
nonlinearity=lasagne.nonlinearities.rectify)

l_hidden2 = lasagne.layers.DenseLayer(
# The first argument is the input layer
l_hidden1,
# This defines the layer's output dimensionality
num_units=100,
# Various nonlinearities are available
nonlinearity=lasagne.nonlinearities.rectify)

l_hidden3 = lasagne.layers.DenseLayer(
# The first argument is the input layer
l_hidden2,
# This defines the layer's output dimensionality
num_units=50,
# Various nonlinearities are available
nonlinearity=lasagne.nonlinearities.rectify)

l_hidden4 = lasagne.layers.DenseLayer(
# The first argument is the input layer
l_hidden3,
# This defines the layer's output dimensionality
num_units=10,
# Various nonlinearities are available
nonlinearity=lasagne.nonlinearities.sigmoid)

# For our output layer, we'll use a dense layer with a softmax nonlinearity.
l_output = lasagne.layers.DenseLayer(
l_hidden4, num_units=N_CLASSES, nonlinearity=lasagne.nonlinearities.softmax)

net_output = lasagne.layers.get_output(l_output)

# As a loss function, we'll use Theano's categorical_crossentropy function.
# This allows for the network output to be class probabilities,
# but the target output to be class labels.
true_output = T.ivector('true_output')

# get_loss computes a Theano expression for the objective,
# given a target variable
# By default, it will use the network's InputLayer input_var,
# which is what we want.
#loss = objective.get_loss(target=true_output)

loss = lasagne.objectives.categorical_crossentropy(net_output, true_output)
loss = aggregate(loss, mode='mean')

# Retrieving all parameters of the network is done using get_all_params,
# which recursively collects the parameters of all layers
# connected to the provided layer.
all_params = lasagne.layers.get_all_params(l_output)

# Now, we'll generate updates using Lasagne's SGD function
updates = lasagne.updates.sgd(loss, all_params, learning_rate=1)

# Finally, we can compile Theano functions for training and
# computing the output.
# Note that because loss depends on the input variable of our input layer,
# we need to retrieve it and tell Theano to use it.
train = theano.function([l_in.input_var, true_output], loss,     updates=updates)
get_output = theano.function([l_in.input_var], net_output)

def eq(x, y):
if x==y:
    return 1
return 0

print("Training ...")
# Train for 100 epochs
for n in xrange(10):
train(X, y)
y_predicted = np.argmax(get_output(X), axis=1)
correct = reduce(lambda a, b: a+b, map(eq, y_predicted, y))
print("Iteration {} correct prediction {}".format(n, correct))

# Compute the predicted label of the training data.
# The argmax converts the class probability output to class label
y_predicted = np.argmax(get_output(X), axis=1)

print(y_predicted)

Answer 1

学习率似乎太高了。首先尝试较低的学习率。您的模型可能在任务上存在分歧。如果无法在您的数据上进行尝试，则很难判断。