SINGA-349 Create layer operations for autograd 1.realize a simple convolution network based on autograd for test use.
2.the code is runnable on my computer, the training effect is obvious, the network parameters explainable. Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/e4610e03 Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/e4610e03 Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/e4610e03 Branch: refs/heads/master Commit: e4610e0379d387169c0aaf4d81f050318933ff09 Parents: 51c242b Author: xuewanqi <36396136+xuewa...@users.noreply.github.com> Authored: Fri May 4 17:31:02 2018 +0800 Committer: Wang Wei <dcs...@nus.edu.sg> Committed: Thu May 17 21:19:06 2018 +0800 ---------------------------------------------------------------------- examples/autograd/mnist_cnn.py | 116 ++++++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/e4610e03/examples/autograd/mnist_cnn.py ---------------------------------------------------------------------- diff --git a/examples/autograd/mnist_cnn.py b/examples/autograd/mnist_cnn.py new file mode 100644 index 0000000..e488bac --- /dev/null +++ b/examples/autograd/mnist_cnn.py @@ -0,0 +1,116 @@ +import numpy as np +from singa import convolution_operation as layer_ops +from singa import tensor +from singa import autograd +from singa import optimizer + + +def load_data(path): + f = np.load(path) + x_train, y_train = f['x_train'], f['y_train'] + x_test, y_test = f['x_test'], f['y_test'] + f.close() + return (x_train, y_train), (x_test, y_test) + +def to_categorical(y, num_classes): + ''' + Converts a class vector (integers) to binary class matrix. + + Args + y: class vector to be converted into a matrix + (integers from 0 to num_classes). + num_classes: total number of classes. + + Return + A binary matrix representation of the input. + ''' + y = np.array(y, dtype='int') + n = y.shape[0] + categorical = np.zeros((n, num_classes)) + categorical[np.arange(n), y] = 1 + categorical=categorical.astype(np.float32) + return categorical + +def preprocess(data): + data=data.astype(np.float32) + data /= 255 + data=np.expand_dims(data, axis=1) + return data + +def accuracy(pred,target): + y = np.argmax(pred, axis=1) + t = np.argmax(target, axis=1) + a = y == t + return np.array(a, 'int').sum() / float(len(t)) + + +if __name__ == '__main__': + + batch_number=600 + num_classes = 10 + epochs = 1 + + sgd = optimizer.SGD(0.05) + #opt = optimizer.SGD(momentum=0.9, weight_decay=1e-4) + + train,test=load_data('/Users/wanqixue/Downloads/mnist.npz') + x_train=preprocess(train[0]) + y_train = to_categorical(train[1], num_classes) + + x_test=preprocess(test[0]) + y_test=to_categorical(test[1],num_classes) + print 'the shape of training data is',x_train.shape + print 'the shape of training label is',y_train.shape + print 'the shape of testing data is', x_test.shape + print 'the shape of testing label is', y_test.shape + + + conv1=layer_ops.Convolution2D('conv1',32,3,1,border_mode='same') + conv2=layer_ops.Convolution2D('conv2',32,3,1,border_mode='same') + + #operations can create when call + relu1=layer_ops.Activation('relu1') + relu2 = layer_ops.Activation('relu2') + pooling= layer_ops.MaxPooling2D('pooling',3,1,border_mode='same') + flatten=layer_ops.Flatten('flatten') + matmul=tensor.Matmul() + add_bias=tensor.AddBias() + softmax=tensor.SoftMax() + cross_entropy=tensor.CrossEntropy() + #avoid repeat create operations + + w = tensor.Tensor(shape=(25088, 10), requires_grad=True, stores_grad=True) #package a dense layer to calculate the shape automatically + w.gaussian(0.0, 0.1) + + b = tensor.Tensor(shape=(1, 10), requires_grad=True, stores_grad=True) + b.set_value(0.0) + + def forward(x,t): + y=conv1(x)[0] + y=relu1(y)[0] + y=conv2(y)[0] + y=relu2(y)[0] + y=pooling(y)[0] + y=flatten(y)[0] + y=matmul(y,w)[0] + y=add_bias(y,b)[0] + y=softmax(y)[0] + loss=cross_entropy(y,t)[0] + return loss, y + + for epoch in range(epochs): + #for i in range(batch_number): + for i in range(50): + inputs = tensor.Tensor(data=x_train[i * 100:(1 + i) * 100, :], requires_grad=False, stores_grad=False) + targets = tensor.Tensor(data=y_train[i * 100:(1 + i) * 100, :], requires_grad=False, stores_grad=False) + loss, y = forward(inputs,targets) + + accuracy_rate = accuracy(tensor.ctensor2numpy(y.data),tensor.ctensor2numpy(targets.data)) + if (i % 5 == 0): + print 'accuracy is:', accuracy_rate,'loss is:', tensor.ctensor2numpy(loss.data)[0] + + in_grads = autograd.backward(loss) + + for param in in_grads: + sgd.apply(0, in_grads[param], param, '') +
