Adding network3.py and expand_mnist.py

mnielsen · mnielsen · commit e1f1bf2ba23a · 2014-12-30T10:35:06.000-05:00
diff --git a/.gitignore b/.gitignore
@@ -2,4 +2,6 @@
 *.org
 *.pkl
 *.pyc
-.DS_Store
+.DS_Store
+loc.py
+src/ec2
diff --git a/src/expand_mnist.py b/src/expand_mnist.py
@@ -0,0 +1,60 @@
+"""expand_mnist.py
+~~~~~~~~~~~~~~~~~~
+
+Take the 50,000 MNIST training images, and create an expanded set of
+250,000 images, by displacing each training image up, down, left and
+right, by one pixel.  Save the resulting file to
+../data/mnist_expanded.pkl.gz.
+
+Note that this program is memory intensive, and may not run on small
+systems.
+
+"""
+
+from __future__ import print_function
+
+#### Libraries
+
+# Standard library
+import cPickle
+import gzip
+import os.path
+import random
+
+# Third-party libraries
+import numpy as np
+
+print("Expanding the MNIST training set")
+
+if os.path.exists("../data/mnist_expanded.pkl.gz"):
+    print("The expanded training set already exists.  Exiting.")
+else:
+    f = gzip.open("../data/mnist.pkl.gz", 'rb')
+    training_data, validation_data, test_data = cPickle.load(f)
+    f.close()
+    expanded_training_pairs = []
+    j = 0 # counter
+    for x, y in zip(training_data[0], training_data[1]):
+        expanded_training_pairs.append((x, y))
+        image = np.reshape(x, (-1, 28))
+        j += 1
+        if j % 1000 == 0: print("Expanding image number", j)
+        # iterate over data telling us the details of how to
+        # do the displacement
+        for d, axis, index_position, index in [
+                (1,  0, "first", 0),
+                (-1, 0, "first", 27),
+                (1,  1, "last",  0),
+                (-1, 1, "last",  27)]:
+            new_img = np.roll(image, d, axis)
+            if index_position == "first": 
+                new_img[index, :] = np.zeros(28)
+            else: 
+                new_img[:, index] = np.zeros(28)
+            expanded_training_pairs.append((np.reshape(new_img, 784), y))
+    random.shuffle(expanded_training_pairs)
+    expanded_training_data = [list(d) for d in zip(*expanded_training_pairs)]
+    print("Saving expanded data. This may take a few minutes.")
+    f = gzip.open("../data/mnist_expanded.pkl.gz", "w")
+    cPickle.dump((expanded_training_data, validation_data, test_data), f)
+    f.close()
diff --git a/src/network3.py b/src/network3.py
@@ -0,0 +1,304 @@
+"""network3.py
+~~~~~~~~~~~~~~
+
+A Theano-based program for training and running simple neural
+networks.
+
+Supports several layer types (fully connected, convolutional, max
+pooling, softmax), and activation functions (sigmoid, tanh, and
+rectified linear units, with more easily added).
+
+When run on a CPU, this program is much faster than network.py and
+network2.py.  However, unlike network.py and network2.py it can also
+be run on a GPU, which makes it faster still.
+
+Because the code is based on Theano, the code is different in many
+ways from network.py and network2.py.  However, where possible I have
+tried to maintain consistency with the earlier programs.  In
+particular, the API is similar to network2.py.  Note that I have
+focused on making the code simple, easily readable, and easily
+modifiable.  It is not optimized, and omits many desirable features.
+
+"""
+
+#### Libraries
+# Standard library
+import cPickle
+import gzip
+
+# Third-party libraries
+import numpy as np
+import theano
+import theano.tensor as T
+from theano.tensor.nnet import conv
+from theano.tensor.nnet import softmax
+from theano.tensor.signal import downsample
+
+# Activation functions for neurons
+def linear(z): return z
+def ReLU(z): return T.maximum(0, z)
+from theano.tensor.nnet import sigmoid
+from theano.tensor import tanh
+
+
+#### Constants
+GPU = False
+if GPU:
+    print "Trying to run under a GPU.  If this is not desired, then modify "+\
+        "network3.py\nto set the GPU flag to False."
+    try: theano.config.device = 'gpu'
+    except: pass # it's already set
+    theano.config.floatX = 'float32'
+
+def example(mini_batch_size=10):
+    print("Loading the MNIST data")
+    training_data, validation_data, test_data = load_data_shared("../data/mnist.pkl.gz")
+    print("Building the network")
+    net = create_net(10)
+    print("Training the network")
+    try:
+        net.SGD(training_data, 200, mini_batch_size, 0.1, 
+                validation_data, test_data, lmbda=1.0)
+    except KeyboardInterrupt:
+        pass
+    return net
+
+def create_net(mini_batch_size=10, activation_fn=tanh):
+    return Network(
+        [ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), filter_shape=(20, 1, 5, 5), poolsize=(2, 2), activation_fn=activation_fn),
+         #ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12), filter_shape=(40, 20, 5, 5), poolsize=(2, 2), activation_fn=activation_fn),
+         #FullyConnectedLayer(n_in=40*4*4, n_out=100, mini_batch_size=mini_batch_size, activation_fn=activation_fn),
+         #FullyConnectedLayer(n_in=784, n_out=100, mini_batch_size=mini_batch_size, activation_fn=activation_fn),
+         #FullyConnectedLayer(n_in=20*12*12, n_out=100, mini_batch_size=mini_batch_size),
+         #FullyConnectedLayer(n_in=100, n_out=100, mini_batch_size=mini_batch_size, activation_fn=activation_fn),
+         #SoftmaxLayer(n_in=100, n_out=10, mini_batch_size=mini_batch_size)], mini_batch_size)
+         SoftmaxLayer(n_in=20*12*12, n_out=10)], mini_batch_size)
+
+#### Load the MNIST data
+def load_data_shared(filename="../data/mnist.pkl.gz"):
+    f = gzip.open(filename, 'rb')
+    training_data, validation_data, test_data = cPickle.load(f)
+    f.close()
+    def shared(data):
+        """Place the data into shared variables.  This allows Theano to copy
+        the data to the GPU, if one is available.
+
+        """
+        shared_x = theano.shared(
+            np.asarray(data[0], dtype=theano.config.floatX), borrow=True)
+        shared_y = theano.shared(
+            np.asarray(data[1], dtype=theano.config.floatX), borrow=True)
+        return shared_x, T.cast(shared_y, "int32")
+    return [shared(training_data), shared(validation_data), shared(test_data)]
+
+#### Main class used to construct and train networks
+class Network():
+    
+    def __init__(self, layers, mini_batch_size):
+        """Takes a list of `layers`, describing the network architecture, and
+        a value for the `mini_batch_size` to be used during training
+        by stochastic gradient descent.
+
+        """
+        self.layers = layers
+        self.mini_batch_size = mini_batch_size
+        self.params = [param for layer in self.layers for param in layer.params]
+        self.x = T.matrix("x")  
+        self.y = T.ivector("y")
+        init_layer = self.layers[0]
+        init_layer.set_inpt(self.x, mini_batch_size)
+        for j in xrange(1, len(self.layers)):
+            prev_layer, layer  = self.layers[j-1], self.layers[j]
+            layer.set_inpt(prev_layer.output, mini_batch_size)
+        self.output = self.layers[-1].output
+
+    def SGD(self, training_data, epochs, mini_batch_size, eta, 
+            validation_data, test_data, lmbda=0.0):
+        """Train the network using mini-batch stochastic gradient descent."""
+        training_x, training_y = training_data
+        validation_x, validation_y = validation_data
+        test_x, test_y = test_data
+
+        # compute number of minibatches for training, validation and testing
+        num_training_batches = size(training_data)/mini_batch_size
+        num_validation_batches = size(validation_data)/mini_batch_size
+        num_test_batches = size(test_data)/mini_batch_size
+
+        # define the (regularized) cost function, symbolic gradients, and updates
+        l2_norm_squared = sum([(layer.w**2).sum() for layer in self.layers])
+        cost = self.log_likelihood()+0.5*lmbda*l2_norm_squared/num_training_batches
+        grads = T.grad(cost, self.params)
+        updates = [(param, param-eta*grad) 
+                   for param, grad in zip(self.params, grads)]
+
+        # define functions to train a mini-batch, and to compute the
+        # accuracy in validation and test mini-batches.
+        i = T.lscalar() # mini-batch index
+        train_mb = theano.function(
+            [i], cost, updates=updates,
+            givens={
+                self.x:
+                training_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
+                self.y: 
+                training_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
+            })
+        validate_mb_accuracy = theano.function(
+            [i], self.layers[-1].accuracy(self.y),
+            givens={
+                self.x: 
+                validation_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
+                self.y: 
+                validation_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
+            })
+        test_mb_accuracy = theano.function(
+            [i], self.layers[-1].accuracy(self.y),
+            givens={
+                self.x: 
+                test_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
+                self.y: 
+                test_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
+            })
+
+        # Do the actual training
+        best_validation_accuracy = 0.0
+        for epoch in xrange(epochs):
+            for minibatch_index in xrange(num_training_batches):
+                iteration = num_training_batches*epoch+minibatch_index
+                if iteration % 1000 == 0: 
+                    print("Training mini-batch number {0}".format(iteration))
+                cost_ij = train_mini_batch(minibatch_index)
+                if (iteration+1) % num_training_batches == 0:
+                    validation_accuracy = np.mean(
+                        [validate_mb_accuracy(j) for j in xrange(num_validation_batches)])
+                    print("Epoch {0}: validation accuracy {1:.2%}".format(
+                        epoch, validation_accuracy))
+                    if validation_accuracy >= best_validation_accuracy:
+                        print("This is the best validation accuracy to date.")
+                        best_validation_accuracy = validation_accuracy
+                        best_iteration = iteration
+                        test_accuracy = np.mean(
+                            [test_mb_accuracy(j) for j in xrange(num_test_batches)])
+                        print('The corresponding test accuracy is {0:.2%}'.format(
+                            test_accuracy))
+        print("Finished training network.")
+        print("Best validation accuracy of {0:.2%} obtained at iteration {1}".format(
+              best_validation_accuracy, best_iteration))
+        print("Corresponding test accuracy of {0:.2%}".format(test_accuracy))
+
+    def log_likelihood(self):
+        "Return the log-likelihood cost."
+        return -T.mean(T.log(self.output)[T.arange(self.y.shape[0]), self.y])
+
+
+#### Define layer types
+
+class ConvPoolLayer():
+    """Used to create a combination of a convolutional and a max-pooling
+    layer.  A more sophisticated implementation would separate the
+    two, but for our purposes we'll always use them together, and it
+    simplifies the code, so it makes sense to combine them.
+
+    """
+
+    def __init__(self, filter_shape, image_shape, poolsize=(2, 2), 
+                 activation_fn=sigmoid):
+        """`filter_shape` is a tuple of length 4, whose entries are the number
+        of filters, the number of input feature maps, the filter height, and the 
+        filter width.
+
+        `image_shape` is a tuple of length 4, whose entries are the
+        mini-batch size, the number of input feature maps, the image
+        height, and the image width.
+
+        `poolsize` is a tuple of length 2, whose entries are the y and
+        x pooling sizes.
+
+        """
+        self.inpt = None
+        self.output = None
+        self.filter_shape = filter_shape
+        self.image_shape = image_shape
+        self.poolsize = poolsize
+        self.activation_fn=activation_fn
+        # initialize weights and biases
+        n_out = (filter_shape[0]*np.prod(filter_shape[2:])/np.prod(poolsize))
+        self.w = theano.shared(
+            np.asarray(
+                np.random.normal(loc=0, scale=np.sqrt(1.0/n_out), size=filter_shape),
+                dtype=theano.config.floatX),
+            borrow=True)
+        self.b = theano.shared(
+            np.asarray(
+                np.random.normal(loc=0, scale=1.0, size=(filter_shape[0],)),
+                dtype=theano.config.floatX),
+            borrow=True)
+        self.params = [self.w, self.b]
+
+    def set_inpt(self, inpt, mini_batch_size):
+        self.inpt = inpt.reshape(self.image_shape)
+        conv_out = conv.conv2d(
+            input=self.inpt, filters=self.w, filter_shape=self.filter_shape,
+            image_shape=self.image_shape)
+        pooled_out = downsample.max_pool_2d(
+            input=conv_out, ds=self.poolsize, ignore_border=True)
+        self.output = self.activation_fn(
+            pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
+
+
+class FullyConnectedLayer():
+
+    def __init__(self, n_in, n_out, mini_batch_size=10, activation_fn=sigmoid):
+        self.n_in = n_in
+        self.n_out = n_out
+        self.activation_fn = activation_fn
+        self.inpt = None
+        self.output = None
+        # Initialize weights and biases
+        self.w = theano.shared(
+            np.asarray(
+                np.random.normal(
+                    loc=0.0, scale=np.sqrt(1.0/n_out), size=(n_in, n_out)),
+                dtype=theano.config.floatX),
+            name='w', borrow=True)
+        self.b = theano.shared(
+            np.asarray(np.random.normal(loc=0.0, scale=1.0, size=(n_out,)),
+                       dtype=theano.config.floatX),
+            name='b', borrow=True)
+        self.params = [self.w, self.b]
+
+    def set_inpt(self, inpt, mini_batch_size):
+        self.mini_batch_size = mini_batch_size
+        self.inpt = inpt.reshape((self.mini_batch_size, self.n_in))
+        self.output = self.activation_fn(T.dot(inpt, self.w)+self.b)
+
+class SoftmaxLayer():
+
+    def __init__(self, n_in, n_out):
+        self.inpt = None
+        self.output = None
+        self.n_in = n_in
+        self.n_out = n_out
+        # Initialize weights and biases
+        self.w = theano.shared(
+            np.zeros((n_in, n_out), dtype=theano.config.floatX),
+            name='w', borrow=True)
+        self.b = theano.shared(
+            np.zeros((n_out,), dtype=theano.config.floatX),
+            name='b', borrow=True)
+        self.params = [self.w, self.b]
+
+    def set_inpt(self, inpt, mini_batch_size):
+        self.mini_batch_size = mini_batch_size
+        self.inpt = inpt.reshape((self.mini_batch_size, self.n_in))
+        self.output = softmax(T.dot(self.inpt, self.w)+self.b)
+        self.y_out = T.argmax(self.output, axis=1)
+
+    def accuracy(self, y):
+        "Return the accuracy for the mini-batch."
+        return T.mean(T.eq(y, self.y_out))
+
+
+#### Miscellanea
+def size(data):
+    "Return the size of the dataset `data`."
+    return data[0].get_value(borrow=True).shape[0]
