nudles commented on a change in pull request #626: [WIP] SINGA-505 Computational graph with memory optimization URL: https://github.com/apache/singa/pull/626#discussion_r393497630
########## File path: examples/autograd/mlp_buffer.py ########## @@ -0,0 +1,112 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from singa import tensor +from singa.tensor import Tensor +from singa import autograd +from singa import optimizer +from singa import device +import numpy as np + + +if __name__ == "__main__": + dev = device.get_default_device() + + autograd.training = True + np.random.seed(0) + + # prepare training data in numpy array + + # generate the boundary + f = lambda x: (5 * x + 1) + bd_x = np.linspace(-1.0, 1, 200) + bd_y = f(bd_x) + # generate the training data + x = np.random.uniform(-1, 1, 400) + y = f(x) + 2 * np.random.randn(len(x)) + # convert training data to 2d space + label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)]) + data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32) + + 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 + return categorical + + label = to_categorical(label, 2).astype(np.float32) + print("train_data_shape:", data.shape) + print("train_label_shape:", label.shape) + + inputs = Tensor(data=data, device=dev) + target = Tensor(data=label, device=dev) + + w0 = Tensor(shape=(2, 3), device=dev, requires_grad=True, stores_grad=True) + w0.gaussian(0.0, 0.1) + b0 = Tensor(shape=(1, 3), device=dev, requires_grad=True, stores_grad=True) + b0.set_value(0.0) + + w1 = Tensor(shape=(3, 2), device=dev, requires_grad=True, stores_grad=True) + w1.gaussian(0.0, 0.1) + b1 = Tensor(shape=(1, 2), device=dev, requires_grad=True, stores_grad=True) + b1.set_value(0.0) + + print("finished init inputs") + print("w0:\n", tensor.to_numpy(w0)) + print("b0:\n", tensor.to_numpy(b0)) + print("w1:\n", tensor.to_numpy(w1)) + print("b1:\n", tensor.to_numpy(b1)) + + sgd = optimizer.SGD(0.05) + + # training process + print("start training") + + # Buffer the operations + dev.EnableGraph(True) Review comment: it would be better to make the code the same for buffer=On or Off. then we can switch between them easily. now this piece of code works when we use buffer + graph. if we do not use graph, we need to write another piece of code. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
