Are sure all your filters and input images are in floats? Something seem to be in int64.
Also, be sure to update Theano to the dev version. Fred Le 30 août 2016 05:41, <[email protected]> a écrit : > Hi, everyone: > I am writing CNN code but there is an error when I compute T.grad in the > Theano. Hopefully you can help me to analysis it. I don't know why there is > an error and how should I modify it. If you can help me, I will be > appreciate of you! Here is some details of dataset. There is a lot of > images which has R,G,B for each. And both width and height of each image > are 50. I am waiting for your reply :) > Here is my code > > # function definition for CNN > srng = RandomStreams() > def floatX(X): > return np.asarray(X, dtype=theano.config.floatX) > > def init_weights(shape): > return theano.shared(floatX(np.random.randn(*shape) * 0.01)) > > def dropout(X, p_use=1.): > if p_use < 1: > p_sampled = srng.binomial(p=p_use, n=1, size=X.shape, > dtype=theano.config.floatX) > X = X * p_sampled / p_use > return X > > def rectify(X): > return T.maximum(X, 0.) > > def PRelu(X,a): > return T.maximum(X, 0.) + a * T.minimum(X, 0.) > > def softmax(X): > e_x = T.exp(X - X.max(axis=1).dimshuffle(0, 'x')) > print e_x > return e_x / e_x.sum(axis=1).dimshuffle(0, 'x') > > def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6): > grads = T.grad(cost=cost, wrt=params) > > updates = [] > for p, g in zip(params, grads): > acc = theano.shared(p.get_value() * 0.) > acc_new = rho * acc + (1 - rho) * g ** 2 > gradient_scaling = T.sqrt(acc_new + epsilon) > g = g / gradient_scaling > updates.append((acc, acc_new)) > updates.append((p, p - lr * g)) > return updates > > # model building > X = T.ftensor4('x') > Y = T.fmatrix('y') > > # parameters initialization > X_train = X_train.reshape(-1, 3, 50, 50) > X_test = X_test.reshape(-1, 3, 50, 50) > W_conv1 = init_weights((4, 3, 5, 5)) > b_conv1 = np.zeros((4,)) > W_conv2 = init_weights((6, 4, 3, 3)) > b_conv2 = np.zeros((6,)) > W_fcn = init_weights((54, 70)) > b_fcn = np.zeros((70,)) > W_fcn2 = init_weights((70, 43)) > b_fcn2 = np.zeros((43,)) > > # convolution and pooling > maxpool_shape = (2, 2) > p_drop_input = 0.8 > conv_layer1 = rectify(conv2d(X_train, W_conv1, border_mode='full')) > subsampling_layer1 = pool_2d(conv_layer1, maxpool_shape, ignore_border=True) > out_layer1 = subsampling_layer1 > out_layer1 = dropout(subsampling_layer1, p_drop_input) > > p_drop_hidden = 0.6 > conv_layer2 = rectify(conv2d(out_layer1, W_conv2, border_mode='valid')) > subsampling_layer2 = pool_2d(conv_layer2, maxpool_shape, ignore_border=True) > out_layer2 = dropout(subsampling_layer2, p_drop_hidden) > conv_out = T.flatten(out_layer2, outdim = 2) > > # fully connected NN > hidden = rectify(T.dot(conv_out, W_fcn)) > hidden = dropout(hidden, p_drop_hidden) > > py_x = softmax(T.dot(hidden, W_fcn2)) > y_x = T.argmax(py_x, axis=1) > > # compute cost and update > cost = T.mean(T.nnet.categorical_crossentropy(py_x , Y)) > params = [W_conv1, W_conv2, W_fcn, W_fcn2] > print cost > print params > updates = RMSprop(cost, params, lr=0.001) > > And the error is that: > > Traceback (most recent call last): > File "/PycharmProjects/CNN/CNN.py", line 180, in <module> > updates = RMSprop(cost, params, lr=0.001) > File "/PycharmProjects/CNN/CNN.py", line 116, in RMSprop > grads = T.grad(cost=cost, wrt=params) > File "/Library/Python/2.7/site-packages/theano/gradient.py", line 561, in > grad > grad_dict, wrt, cost_name) > File "/Library/Python/2.7/site-packages/theano/gradient.py", line 1324, in > _populate_grad_dict > rval = [access_grad_cache(elem) for elem in wrt] > File "/Library/Python/2.7/site-packages/theano/gradient.py", line 1279, in > access_grad_cache > term = access_term_cache(node)[idx] > File "/Library/Python/2.7/site-packages/theano/gradient.py", line 1113, in > access_term_cache > input_grads = node.op.grad(inputs, new_output_grads) > File > "/Library/Python/2.7/site-packages/theano/tensor/nnet/abstract_conv.py", line > 828, in grad > d_bottom = bottom.type.filter_variable(d_bottom) > File "/Library/Python/2.7/site-packages/theano/tensor/type.py", line 233, > in filter_variable > self=self)) > TypeError: Cannot convert Type TensorType(float64, 4D) (of Variable > AbstractConv2d_gradInputs{border_mode='full', subsample=(1, 1), > filter_flip=True, imshp=(None, None, None, None), kshp=(None, None, None, > None)}.0) into Type TensorType(int64, 4D). You can try to manually convert > AbstractConv2d_gradInputs{border_mode='full', subsample=(1, 1), > filter_flip=True, imshp=(None, None, None, None), kshp=(None, None, None, > None)}.0 into a TensorType(int64, 4D). > > Process finished with exit code 1 > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
