您好我正在尝试修改mnist示例以将其与我的数据集相匹配。我只尝试使用mlp示例，它给出了一个奇怪的错误。
Tha数据集是一个包含2100行和17列的矩阵，输出应该是16个可能的类之一。这个错误似乎发生在培训的第二阶段。模型构建正确（已确认日志信息）。
以下是错误日志：
ValueError：y_i值超出范围
应用导致错误的节点：
CrossentropySoftmaxArgmax1HotWithBias（Dot22.0，b，targets）
Toposort指数：33
输入类型：[TensorType（float64，matrix），TensorType（float64，vector），＆gt; TensorType（int32，vector）]
输入形状：[（100,17），（17，），（100，）]
输入步幅：[（136,8），（8，），（4，）]
输入值：['未显示'，'未显示'，'未显示']
输出客户端：[[Sum {acc_dtype = float64}（CrossentropySoftmaxArgmax1HotWithBias.0）]，[CrossentropySoftmax1HotWithBiasDx（Assert {msg ='
sm和dy的形状不同。'}。 0，CrossentropySoftmaxArgmax1HotWithBias.1，targets）]，[]]提示：禁用大多数Theano优化后重新运行可以为您提供创建此节点时的＆gt;回溯。这可以通过＆gt;设置Theano标志'optimizer = fast_compile'来完成。如果这不起作用，可以使用'optimizer = None'禁用＆a; Theano优化。 提示：将Theano标志'exception_verbosity = high'用于此apply节点的调试打印和存储映射占用空间。
以下是代码：
def build_mlp(input_var=None):
l_in = lasagne.layers.InputLayer(shape=(None, 16),
input_var=input_var)
# Apply 20% dropout to the input data:
l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.2)
# Add a fully-connected layer of 800 units, using the linear rectifier, and
# initializing weights with Glorot's scheme (which is the default anyway):
l_hid1 = lasagne.layers.DenseLayer(
l_in_drop, num_units=10,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
# We'll now add dropout of 50%:
l_hid1_drop = lasagne.layers.DropoutLayer(l_hid1, p=0.5)
# Another 800-unit layer:
l_hid2 = lasagne.layers.DenseLayer(
l_hid1_drop, num_units=10,
nonlinearity=lasagne.nonlinearities.rectify)
# 50% dropout again:
l_hid2_drop = lasagne.layers.DropoutLayer(l_hid2, p=0.5)
# Finally, we'll add the fully-connected output layer, of 10 softmax units:
l_out = lasagne.layers.DenseLayer(
l_hid2_drop, num_units=17,
nonlinearity=lasagne.nonlinearities.softmax)
# Each layer is linked to its incoming layer(s), so we only need to pass
# the output layer to give access to a network in Lasagne:
return l_out
def main(model='mlp', num_epochs=300):
# Load the dataset
print("Loading data...")
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset()
# Prepare Theano variables for inputs and targets
input_var = T.matrix('inputs')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
if model == 'cnn':
network = build_cnn(input_var)
elif model == 'mlp':
network = build_mlp(input_var)
elif model == 'lstm':
network = build_lstm(input_var)
else:
print("Unrecognized model type %r." % model)
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
# We could add some weight decay as well here, see lasagne.regularization.
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate=0.01, momentum=0.9)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
# We iterate over epochs:
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train, y_train, 100, shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val, y_val, 100, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
print(" validation accuracy:\t\t{:.2f} %".format(
val_acc / val_batches * 100))
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, 100, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_err / test_batches))
print(" test accuracy:\t\t{:.2f} %".format(
test_acc / test_batches * 100))
答案 0 :(得分：3)
我想出了问题： 我的数据集没有每个目标的输出，因为它太小了！有17个目标输出但我的数据集只有16个不同的输出，它缺少第17个输出的例子。
要解决此问题，只需更改softmax并进行纠正，
来自：
l_out = lasagne.layers.DenseLayer(
l_hid2_drop, num_units=17,
nonlinearity=lasagne.nonlinearities.softmax)
到此：
l_out = lasagne.layers.DenseLayer(
l_hid2_drop, num_units=17,
nonlinearity=lasagne.nonlinearities.rectify)