ValueError: 'total size of new array must be unchanged'

Am I doing something wrong here:

net1 = NeuralNet(
    layers=[  # three layers: one hidden layer
        ('input', layers.InputLayer),
        ('conv1', layers.Conv2DLayer),
        ('pool1', layers.MaxPool2DLayer),
        ('dropout1', layers.DropoutLayer),
        ('hidden', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
    # layer parameters:
    input_shape=(32, 1, 300, 400),  # 32 images per batch times
    hidden_num_units=100,  # number of units in hidden layer
    output_nonlinearity=None,  # output layer uses identity function
    output_num_units=len(classes), 

    # optimization method:
    upate=nesterov_momentum,
    update_learning_rate=0.01,
    update_momentum=0.9,

    regression=False,  # flag to indicate we're not dealing with regression problem
    use_label_encoder=True,
    max_epochs=400,  # we want to train this many epochs
    verbose=1,
    batch_iterator=LoadBatchIterator(batch_size=32),

    conv1_num_filters=4, conv1_filter_size=(3, 3), pool1_ds=(2, 2),
    dropout1_p=0.1,
    )

leads to:

/home/ubuntu/git/nolearn/nolearn/lasagne.pyc in fit(self, X, y)
    155 
    156         try:
--> 157             self.train_loop(X, y)
    158         except KeyboardInterrupt:
    159             pdb.set_trace()

/home/ubuntu/git/nolearn/nolearn/lasagne.pyc in train_loop(self, X, y)
    193 
    194             for Xb, yb in self.batch_iterator(X_train, y_train):
--> 195                 batch_train_loss = self.train_iter_(Xb, yb)
    196                 train_losses.append(batch_train_loss)
    197 

/home/ubuntu/git/Theano/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
    603                     gof.link.raise_with_op(
    604                         self.fn.nodes[self.fn.position_of_error],
--> 605                         self.fn.thunks[self.fn.position_of_error])
    606                 else:
    607                     # For the c linker We don't have access from

/home/ubuntu/git/Theano/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
    593         t0_fn = time.time()
    594         try:
--> 595             outputs = self.fn()
    596         except Exception:
    597             if hasattr(self.fn, 'position_of_error'):

/home/ubuntu/git/Theano/theano/gof/op.pyc in rval(p, i, o, n)
    751 
    752         def rval(p=p, i=node_input_storage, o=node_output_storage, n=node):
--> 753             r = p(n, [x[0] for x in i], o)
    754             for o in node.outputs:
    755                 compute_map[o][0] = True

/home/ubuntu/git/Theano/theano/sandbox/cuda/basic_ops.pyc in perform(self, node, inp, out_)
   2349             else:
   2350                 raise ValueError("total size of new array must be unchanged",
-> 2351                                  x.shape, shp)
   2352 
   2353         out[0] = x.reshape(tuple(shp))

ValueError: ('total size of new array must be unchanged', (31, 4, 298, 398), array([128,   1, 298, 398]))
Apply node that caused the error: GpuReshape{4}(GpuElemwise{Composite{[mul(i0, add(i1, Abs(i1)))]},no_inplace}.0, TensorConstant{[128   1 298 398]})
Inputs types: [CudaNdarrayType(float32, 4D), TensorType(int64, vector)]
Inputs shapes: [(31, 4, 298, 398), (4,)]
Inputs strides: [(474416, 118604, 398, 1), (8,)]
Inputs values: ['not shown', array([128,   1, 298, 398])]

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'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint of this apply node.

[dnouri]

Ah, it looks like you’re using the Conv2DLayer implementation that doesn’t like batches that aren’t exactly of size batch_size. The cuda_convnet-based implementation (used in the tutorial) doesn’t have this problem. See here for a discussion with two possible solutions.

[dnouri]

It turns out that there’s a much easier solution to this problem. In the tutorial, I made an error and falsely set the input layer’s shape[0] (the batch size) to be 128. This should have been None. I verified that with this setting the “legacy” Theano convnet layer (for CPU) is happy and every other layer that I tested was too.

So that means I could undo the forced_even change again. Please update your code.