I am using lasagne's Conv3DDNNLayer, and have input dimensions of (N x 1 x 9 x 9 x 9), where each 9x9x9 cube is my sample to be classified.
Therefore I have a target dimension of (N x 1), with each entry corresponding to a cube. This is raising the error:
Bad input argument to theano function with name "Conv_Net_1.py:45" at index 1(0-based)', 'Wrong number of dimensions: expected 1,
got 2 with shape (324640, 1).')´
Which dimensions should I have my targets in in this case?
11 dtensor5 = TensorType('float32', (False,)*5)
12 input_var = dtensor5('X_Train')
13 target_var = T.ivector('Y_train')
14
15 X_train, Y_train = DP.data_gen( '/home/Upload/Smalls', 9)
16 print X_train.shape
17 print Y_train.shape
18 # Build Neural Network:
19 input = lasagne.layers.InputLayer((None, 1, 9, 9, 9), input_var=input_var)
20
21 l_conv_1 = lasagne.layers.dnn.Conv3DDNNLayer(input, 20, (2,2,2))
22
29 l_hidden1 = lasagne.layers.DenseLayer(l_conv_1, num_units=256,nonlinearity=lasagne.nonlinearities.rectify,W=l asagne.init.HeNormal(gain='relu'))
30
31 l_hidden1_dropout = lasagne.layers.DropoutLayer(l_hidden1, p=0.5)
32
33 output = lasagne.layers.DenseLayer(l_hidden1_dropout, num_units=2, nonlinearity = lasagne.nonlinearities.soft max)
34
35 ##
36 prediction = lasagne.layers.get_output(output)
37 loss = T.mean(lasagne.objectives.categorical_crossentropy(prediction, target_var)
39
40 # Get list of all trainable parameters in the network.
41 params = lasagne.layers.get_all_params(output, trainable=True)
42 updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.3)
43
44 ##
45 train_fn = theano.function([input_var, target_var], loss, updates=updates)
46
47 ##
48 for epoch in range(500):
49 print('training')
50 loss = train_fn(X_train, Y_train)
51 print(loss.type)
52 print("Epoch %d: Loss %g" % (epoch + 1, loss))
53
54
55 ##
56 test_prediction = lasagne.layers.get_output(output, deterministic=True)
57 predict_fn = theano.function([input_var], T.argmax(test_prediction, axis=1))
edit - added code
Thanks!
In case any one is interested, it was because the data was (N, 1) not (N, ).
seemed to solve the problem! - on to the next..