# -*- coding: utf-8 -*- """ Created on Sun Sep 1 14:59:45 2019 @author: jeong """ def testTheano(): from theano import function, config, shared, sandbox import theano.tensor as T import numpy import time print("Testing Theano library...") vlen = 10 * 30 * 768 # 10 x #cores x # threads per core iters = 1000 rng = numpy.random.RandomState(22) x = shared(numpy.asarray(rng.rand(vlen), config.floatX)) f = function([], T.exp(x)) print(f.maker.fgraph.toposort()) t0 = time.time() for i in range(iters): r = f() t1 = time.time() print("Looping %d times took %f seconds" % (iters, t1 - t0)) print("Result is %s" % (r,)) if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]): print('Used the cpu') else: print('Used the gpu') # Perform check: #testTheano() # ---------------------- # - network3.py example: import network3 from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer # softmax plus log-likelihood cost is more common in modern image classification networks. # read data: training_data, validation_data, test_data = network3.load_data_shared() # mini-batch size: mini_batch_size = 10 # chapter 6 - rectified linear units and some l2 regularization (lmbda=0.1) => even better accuracy from network3 import ReLU net = Network([ ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), filter_shape=(20, 1, 5, 5), poolsize=(2, 2), activation_fn=ReLU), ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12), filter_shape=(40, 20, 5, 5), poolsize=(2, 2), activation_fn=ReLU), FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU), SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size) net.SGD(training_data, 60, mini_batch_size, 0.03, validation_data, test_data, lmbda=0.1)