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-1.0..1.0の範囲の値を出力するネットワークを学習しようとしています。これまでに6つの機能しかなく、すべてが浮動小数点です。私はタイプと形状を整えるのに本当に問題があります。これまでのところ私が持っている：ラサグで回帰するための寸法を取得する
#!/usr/bin/env python3
import lasagne
import numpy as np
import sys
import theano
import theano.tensor as T
infilename = sys.argv[1]
split_size = 500
epochs = 100
theano.config.exception_verbosity = 'high'
examples = np.genfromtxt(infilename, delimiter=' ')
np.random.shuffle(examples)
examples = examples.reshape(-1, 7)
train, test = examples[:split_size,:], examples[split_size:,:]
# input and target
train_y = train[:,0]
train_X = train[:,1:]
test_y = test[:,0]
test_X = test[:,1:]
input_var = T.matrix()
target_var = T.vector()
def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
# nn structure
from lasagne.nonlinearities import tanh, softmax, leaky_rectify
net = lasagne.layers.InputLayer(shape=(None, 6), input_var=input_var)
net = lasagne.layers.DenseLayer(net, num_units=10, nonlinearity=tanh)
net = lasagne.layers.DenseLayer(net, num_units=1, nonlinearity=softmax)
prediction = lasagne.layers.get_output(net)
loss = lasagne.objectives.aggregate(prediction, target_var)
loss = loss.mean() + 1e-4 * lasagne.regularization.regularize_network_params(net, lasagne.regularization.l2)
# parameter update expressions
params = lasagne.layers.get_all_params(net, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate = 0.02, momentum=0.9)
# training function
train_fn = theano.function([input_var, target_var], loss, updates=updates)
for epoch in range(epochs):
loss = 0
for input_batch, target_batch in iterate_minibatches(train_X, train_y, 50, shuffle=True):
print('input', input_batch.shape)
print('target', target_batch.shape)
loss += train_fn(input_batch, target_batch)
print('epoch', epoch, 'loss', loss/len(training_data))
test_prediction = lasagne.layers.get_output(network, deterministic=True)
predict_fn = theano.function([input_var], T.argmax(test_prediction, axis=1))
print('predicted score for first test input', predict_fn(test_X[0]))
print(net_output)
入力データが山車の7列のファイル、スペースで区切られています。以下にいくつかの例を示します。
-0.4361711835021444 0.9926778242677824 1.0 0.0 0.0 0.0 0.0
1.0 0.9817294281729428 1.0 1.7142857142857142 0.0 0.42857142857142855 1.7142857142857142
-0.4356014580801944 0.9956764295676429 1.0 0.0 0.0 0.0 0.0
1.0 1.0 3.0 0.0 0.0 4.0 1.0
-0.4361977186311787 0.9925383542538354 1.0 0.0 0.0 0.0 0.0
-0.46511627906976744 1.0 0.5 0.0 0.0 0.0 0.0
-0.4347826086956522 1.0 1.0 0.0 0.0 0.0 0.0
-0.4378224895429426 0.9840306834030683 1.0 0.0 0.0 0.0 0.0
-0.4377155764476054 0.9845885634588564 1.0 0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0 0.0 2.0 0.0
これは、ラザニアの参考例に基づいています。出てくるエラーは次のとおりです。lasagne.objectives.squared_errorを使用した場合
/usr/local/lib/python3.5/dist-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
input (50, 6)
target (50,)
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/theano/compile/function_module.py", line 859, in __call__
outputs = self.fn()
ValueError: Input dimension mis-match. (input[0].shape[1] = 1, input[1].shape[1] = 50)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./nn_cluster.py", line 66, in <module>
loss += train_fn(input_batch, target_batch)
File "/usr/local/lib/python3.5/dist-packages/theano/compile/function_module.py", line 871, in __call__
storage_map=getattr(self.fn, 'storage_map', None))
File "/usr/local/lib/python3.5/dist-packages/theano/gof/link.py", line 314, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/usr/lib/python3/dist-packages/six.py", line 685, in reraise
raise value.with_traceback(tb)
File "/usr/local/lib/python3.5/dist-packages/theano/compile/function_module.py", line 859, in __call__
outputs = self.fn()
ValueError: Input dimension mis-match. (input[0].shape[1] = 1, input[1].shape[1] = 50)
Apply node that caused the error: Elemwise{Mul}[(0, 0)](SoftmaxWithBias.0, InplaceDimShuffle{x,0}.0)
Toposort index: 21
Inputs types: [TensorType(float64, matrix), TensorType(float64, row)]
Inputs shapes: [(50, 1), (1, 50)]
Inputs strides: [(8, 8), (400, 8)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[Sum{acc_dtype=float64}(Elemwise{Mul}[(0, 0)].0)]]
Debugprint of the apply node:
Elemwise{Mul}[(0, 0)] [id A] <TensorType(float64, matrix)> ''
|SoftmaxWithBias [id B] <TensorType(float64, matrix)> ''
| |Dot22 [id C] <TensorType(float64, matrix)> ''
| | |Elemwise{Composite{tanh((i0 + i1))}}[(0, 0)] [id D] <TensorType(float64, matrix)> ''
| | | |Dot22 [id E] <TensorType(float64, matrix)> ''
| | | | |<TensorType(float64, matrix)> [id F] <TensorType(float64, matrix)>
| | | | |W [id G] <TensorType(float64, matrix)>
| | | |InplaceDimShuffle{x,0} [id H] <TensorType(float64, row)> ''
| | | |b [id I] <TensorType(float64, vector)>
| | |W [id J] <TensorType(float64, matrix)>
| |b [id K] <TensorType(float64, vector)>
|InplaceDimShuffle{x,0} [id L] <TensorType(float64, row)> ''
|<TensorType(float64, vector)> [id M] <TensorType(float64, vector)>
Storage map footprint:
- Elemwise{Composite{tanh((i0 + i1))}}[(0, 0)].0, Shape: (50, 10), ElemSize: 8 Byte(s), TotalSize: 4000 Byte(s)
- <TensorType(float64, matrix)>, Input, Shape: (50, 6), ElemSize: 8 Byte(s), TotalSize: 2400 Byte(s)
- W, Shared Input, Shape: (6, 10), ElemSize: 8 Byte(s), TotalSize: 480 Byte(s)
- <TensorType(float64, matrix)>, Shared Input, Shape: (6, 10), ElemSize: 8 Byte(s), TotalSize: 480 Byte(s)
- SoftmaxWithBias.0, Shape: (50, 1), ElemSize: 8 Byte(s), TotalSize: 400 Byte(s)
- InplaceDimShuffle{x,0}.0, Shape: (1, 50), ElemSize: 8 Byte(s), TotalSize: 400 Byte(s)
- SoftmaxGrad.0, Shape: (50, 1), ElemSize: 8 Byte(s), TotalSize: 400 Byte(s)
- <TensorType(float64, vector)>, Input, Shape: (50,), ElemSize: 8 Byte(s), TotalSize: 400 Byte(s)
- W, Shared Input, Shape: (10, 1), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
- b, Shared Input, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
- <TensorType(float64, vector)>, Shared Input, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
- <TensorType(float64, matrix)>, Shared Input, Shape: (10, 1), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
- TensorConstant{0.02}, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- b, Shared Input, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- TensorConstant{0.0001}, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{(1, 1) of 0.9}, Shape: (1, 1), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- TensorConstant{4.00000000..000001e-06}, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{(1,) of 0.02}, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- Constant{0}, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Subtensor{int64}.0, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{(1,) of 0.9}, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- Constant{1}, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Subtensor{int64}.0, Shape:(), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{(1, 1) of 1.0}, Shape: (1, 1), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- <TensorType(float64, vector)>, Shared Input, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
TotalSize: 8984.0 Byte(s) 0.000 GB
TotalSize inputs: 4168.0 Byte(s) 0.000 GB
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
同様の例外が発生します。何か案は？データ形状がどこで間違っているのか、それが問題ならば、私はうまくいかず、目的関数を使用するのが正しい方法であるかどうかは分かりません。