Input dimension mismatch binary crossentropy Lasag

Question

I read all posts in the net adressing the issue where people forgot to change the target vector to a matrix, and as a problem remains after this change, I decided to ask my question here. Workarounds are mentioned below, but new problems show and I am thankful for suggestions!

Using a convolution network setup and binary crossentropy with sigmoid activation function, I get a dimension mismatch problem, but not during the training data, only during validation / test data evaluation. For some strange reason, of of my validation set vectors get his dimension switched and I have no idea, why. Training, as mentioned above, works fine. Code follows below, thanks a lot for help (and sorry for hijacking the thread, but I saw no reason for creating a new one), most of it copied from the lasagne tutorial example.

Workarounds and new problems:

1. Removing "axis=1" in the valAcc definition helps, but validation accuracy remains zero and test classification always returns the same result, no matter how many nodes, layers, filters etc. I have. Even changing training set size (I have around 350 samples for each class with 48x64 grayscale images) does not change this. So something seems off

Network creation:

def build_cnn(imgSet, input_var=None):
# As a third model, we'll create a CNN of two convolution + pooling stages
# and a fully-connected hidden layer in front of the output layer.

# Input layer using shape information from training
network = lasagne.layers.InputLayer(shape=(None, \
    imgSet.shape[1], imgSet.shape[2], imgSet.shape[3]), input_var=input_var)
# This time we do not apply input dropout, as it tends to work less well
# for convolutional layers.

# Convolutional layer with 32 kernels of size 5x5. Strided and padded
# convolutions are supported as well; see the docstring.
network = lasagne.layers.Conv2DLayer(
        network, num_filters=32, filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.rectify,
        W=lasagne.init.GlorotUniform())

# Max-pooling layer of factor 2 in both dimensions:
network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

# Another convolution with 16 5x5 kernels, and another 2x2 pooling:
network = lasagne.layers.Conv2DLayer(
        network, num_filters=16, filter_size=(5, 5),
        nonlinearity=lasagne.nonlinearities.rectify)

network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

# A fully-connected layer of 64 units with 25% dropout on its inputs:
network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.25),
        num_units=64,
        nonlinearity=lasagne.nonlinearities.rectify)

# And, finally, the 2-unit output layer with 50% dropout on its inputs:
network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.5),
        num_units=1,
        nonlinearity=lasagne.nonlinearities.sigmoid)

return network

Target matrices for all sets are created like this (training target vector as an example)

 targetsTrain = np.vstack( (targetsTrain, [[targetClass], ]*numTr) );

...and the theano variables as such

inputVar = T.tensor4('inputs')
targetVar = T.imatrix('targets')
network = build_cnn(trainset, inputVar)
predictions = lasagne.layers.get_output(network)
loss = lasagne.objectives.binary_crossentropy(predictions, targetVar)
loss = loss.mean()
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)
valPrediction = lasagne.layers.get_output(network, deterministic=True)
valLoss = lasagne.objectives.binary_crossentropy(valPrediction, targetVar)
valLoss = valLoss.mean()
valAcc = T.mean(T.eq(T.argmax(valPrediction, axis=1), targetVar), dtype=theano.config.floatX)
train_fn = function([inputVar, targetVar], loss, updates=updates,  allow_input_downcast=True)
val_fn = function([inputVar, targetVar], [valLoss, valAcc])

Finally, here the two loops, training and test. The first is fine, the second throws the error, excerpts below

# -- Neural network training itself -- #
numIts = 100
for itNr in range(0, numIts):
train_err = 0
train_batches = 0
for batch in iterate_minibatches(trainset.astype('float32'), targetsTrain.astype('int8'), len(trainset)//4, shuffle=True):
    inputs, targets = batch
    print (inputs.shape)
    print(targets.shape)        
    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(valset.astype('float32'), targetsVal.astype('int8'), len(valset)//3, shuffle=False):
    [inputs, targets] = batch
    [err, acc] = val_fn(inputs, targets)
    val_err += err
    val_acc += acc
    val_batches += 1

Erorr (excerpts)

Exception "unhandled ValueError"
Input dimension mis-match. (input[0].shape[1] = 52, input[1].shape[1] = 1)
Apply node that caused the error: Elemwise{eq,no_inplace}(DimShuffle{x,0}.0, targets)
Toposort index: 36
Inputs types: [TensorType(int64, row), TensorType(int32, matrix)]
Inputs shapes: [(1, 52), (52, 1)]
Inputs strides: [(416, 8), (4, 4)]
Inputs values: ['not shown', 'not shown']

Again, thanks for help!

Answer 1

so it seems the error is in the evaluation of the validation accuracy. When you remove the "axis=1" in your calculation, the argmax goes on everything, returning only a number. Then, broadcasting steps in and this is why you would see the same value for the whole set.

But from the error you have posted, the "T.eq" op throws the error because it has to compare a 52 x 1 with a 1 x 52 vector (matrix for theano/numpy). So, I suggest you try to replace the line with:

    valAcc = T.mean(T.eq(T.argmax(valPrediction, axis=1), targetVar.T))

I hope this should fix the error, but I haven't tested it myself.

EDIT: The error lies in the argmax op that is called. Normally, the argmax is there to determine which of the output units is activated the most. However, in your setting you only have one output neuron which means that the argmax over all output neurons will always return 0 (for first arg).

This is why you have the impression your network gives you always 0 as output.

By replacing:

    valAcc = T.mean(T.eq(T.argmax(valPrediction, axis=1), targetVar.T))

with:

    binaryPrediction = valPrediction > .5
    valAcc = T.mean(T.eq(binaryPrediction, targetVar.T)

you should get the desired result.

I'm just not sure, if the transpose is still necessary or not.