egeromin · January 29, 2018 16:49
diff --git a/fully_connected_net.py b/fully_connected_net.py
 """
 Class to define a fully connected neural net. Written using
 numpy only.

 The number and size of hidden layers is defined by providing
 a list to the constructor, for example

 ```
 net = FullyConnectedNet([1024, 450, 10])
 ```

 In this case, `net` represents a 3-layer neural net, with an input
 unit of size 1024, a hidden unit of size 450, and an output
 unit of size 10.

 The following defaults are hardcoded (for now):

 - The hidden layers have RELU activation
 - The output layer has SOFTMAX activation
 - The cost function is the cross-entropy cost
 - Weights use He initialisation
 - The update optimizer rule is ADAM (but you can choose the parameters)
 - Dropout regularization is used (but you can choose the parameter)

 Also implements *gradient checking* to check that backprop is
 working correctly.
 """
 import os

 import numpy as np
 import matplotlib.pyplot as plt


 class FullyConnectedNet:

    def __init__(self, layer_spec, dropout_prob=0.25, learning_rate=0.003,
                beta1=0.9, beta2=0.999, epsilon=1e-8):
        """Initialise learning hyperparameters"""
        self.layer_spec = layer_spec
        self.dropout_prob = dropout_prob
        self.learning_rate = learning_rate
        self.beta1 = beta1
        self.beta2 = beta2
        self.epsilon = epsilon

    def train_init(self):
        """Initialise all the parameters I require for training"""
        self.bias_init()
        self.weight_init_he()
        self.adam_init()

        self.A, self.Z, self.D, self.costs = [], [], [], []
        self.dW, self.db = [], []

    def bias_init(self):
        """Initialise biases to 0"""
        self.b = [np.zeros((sz_layer, 1)) for sz_layer in self.layer_spec[1:]]

    def weight_init_he(self):
        """Initialise weights using He initialisation"""
        np.random.seed(8)
        self.W = [np.random.randn(self.layer_spec[i], self.layer_spec[i - 1]) * np.sqrt(2 / self.layer_spec[i - 1])
                  for i in range(1, len(self.layer_spec))]

    def weight_init_random(self):
        """Random initialisation of weights, used for debugging"""
        np.random.seed(3)
        self.W = [np.random.randn(self.layer_spec[i], self.layer_spec[i - 1]) * 0.01
                  for i in range(1, len(self.layer_spec))]

    def weight_init_zeros(self):
        """Zeros initialisation of weights, used for debugging"""
        self.W = [np.zeros((self.layer_spec[i], self.layer_spec[i - 1]))
                  for i in range(1, len(self.layer_spec))]
        # only used for debugging!

    def adam_init(self):
        """Initialise adam moving averages"""
        self.t = 1
        self.vdW = [np.zeros(Wi.shape) for Wi in self.W]
        self.vdb = [np.zeros(bi.shape) for bi in self.b]
        self.sdW = [np.zeros(Wi.shape) for Wi in self.W]
        self.sdb = [np.zeros(bi.shape) for bi in self.b]

    def relu(self, x):
        return np.maximum(x, 0)

    def softmax(self, z):
        y = np.exp(z)
        y_soft = y / y.sum(axis=0)
        return y_soft

    def cross_entropy_cost(self, y_soft, labels):
        assert (y_soft.shape == labels.shape)  # labels need to be 1-hot encoded
        m = labels.shape[1]  # number of samples
        cost = -np.multiply(np.log(y_soft), labels).sum() / m
        return cost

    def relu_grad(self, x):
        return x > 0

    def cross_entropy_with_softmax_grad(self, y_soft, labels):
        """Backprops directly to Z's"""
        grad = y_soft - labels
        return grad

    def forward(self, batch, labels=None, dropout=False):
        """
        Do a full forward pass over all layers, for a batch of inputs.
        Computes and stores the cost in `self.costs`
        :param batch: The batch, dimensions: dim_input x num_samples
        :param labels: The labels, 1-hot encoded
        :param dropout: `True` in order to use dropout
        """

        def single_forward(prev_layer, weights, bias, activation_func, with_dropout=False):
            """Given activations of one layer, do a forward pass and
            compute the activations of the next"""
            Z = np.dot(weights, prev_layer) + bias
            A = activation_func(Z)

            if with_dropout:
                D = np.random.rand(*A.shape) > self.dropout_prob
                A = np.multiply(A, D)  # apply dropout mask

            self.Z.append(Z)
            self.A.append(A)
            if with_dropout:
                self.D.append(D)

        self.A.append(batch)  # populate initial 'activations'
        for Wi, bi in zip(self.W[:-1], self.b[:-1]):
            Aprev = self.A[-1]
            single_forward(Aprev, Wi, bi, self.relu, with_dropout=dropout)
            # compute RELU activation for all units

        single_forward(self.A[-1], self.W[-1], self.b[-1], self.softmax)
        # compute sigmoid activation for final layer

        cost = None
        if labels is not None:
            cost = self.cross_entropy_cost(self.A[-1], labels)
            self.costs.append(cost)  # keep track of costs, for debugging and checking

        return self.A[-1], cost  # return final activations and cost

    def backward(self, labels, dropout=False):
        """
        Do a full backward pass over all layers.
        :param labels: The labels used for forward propagation, one-hot encoded.
        """
        self.dW, self.db = [], []  # reset weights
        m = labels.shape[1]  # number of samples in batch

        def single_backward(dA, weights, with_dropout=False, labels=None):
            """Do a backward pass for a single layer.
            The case `labels is not None` is used for last layer, instead of dA.
            Otherwise, relu_grad is used"""
            if with_dropout and dA is not None:
                D = self.D.pop()
                dA = np.multiply(dA, D)  # apply previously created dropout mask to gradient.

            if labels is not None:
                dZ = self.cross_entropy_with_softmax_grad(self.A.pop(), labels)  # gradient for last layer
                self.Z.pop()  # final 'Z' not required, throw it away
            else:
                dZ = np.multiply(self.relu_grad(self.Z.pop()), dA)
            dW = np.dot(dZ, self.A.pop().T) / m
            db = np.sum(dZ, axis=1, keepdims=True) / m
            self.dW.insert(0, dW)
            self.db.insert(0, db)
            return np.dot(weights.T, dZ)  # return updated dA

        dA_current = single_backward(None, self.W[-1], labels=labels)
        # do first backward pass in case 'softmax'

        for Wi in reversed(self.W[:-1]):
            dA_current = single_backward(dA_current, Wi, with_dropout=dropout)
            # do all 'RELU' backward passes

    def check_gradient(self, batch, labels, eps=1e-4, check_thresh=1e-6):
        """Method implementing gradient checking:
        for each example in the batch, compute dW by backprop
        and manually, by computing the partial derivative using
        partial approx= (cost(x+eps) - cost(x-eps)) / 2*eps.

        Return the maximum difference of gradients"""

        self.forward(batch, labels)
        self.backward(labels)  # computes dW

        max_diff = 0.0

        for Wi, dWi in zip(self.W, self.dW):
            dWi_manual = np.zeros(Wi.shape)
            for iter_W in range(len(dWi_manual.flat)):
                Wi.flat[iter_W] += eps
                _, cost_plus = self.forward(batch, labels)
                Wi.flat[iter_W] -= 2*eps
                _, cost_minus = self.forward(batch, labels)
                Wi.flat[iter_W] += eps
                dWi_manual.flat[iter_W] = (cost_plus - cost_minus) / (2*eps)

            max_diff = max(max_diff, abs(dWi_manual - dWi).max())

        if max_diff > check_thresh:
            raise RuntimeError("Net not passing gradient checking, max_diff = {:.4f}. "
                               "Something in the code is probably wrong!".format(max_diff))

    def gradient_update_simple(self):
        """Simple gradient update, for debugging"""
        self.W = [Wi - self.learning_rate * dWi
                  for Wi, dWi in zip(self.W, self.dW)]
        self.b = [bi - self.learning_rate * dbi
                  for bi, dbi in zip(self.b, self.db)]

    def gradient_update_adam(self):
        """Use stored gradients dW to perform an ADAM update of
        the weights W"""

        def v_compute_running_average(vd, grads):
            return [self.beta1 * vdi + (1 - self.beta1) * gradi
                    for vdi, gradi in zip(vd, grads)]

        self.vdW = v_compute_running_average(self.vdW, self.dW)
        self.vdb = v_compute_running_average(self.vdb, self.db)

        def v_compute_correction(vd):
            return [vdi / (1 - np.power(self.beta1, self.t))
                    for vdi in vd]

        vdW_corrected = v_compute_correction(self.vdW)
        vdb_corrected = v_compute_correction(self.vdb)

        def s_compute_running_average(sd, grads):
            return [self.beta2 * sdi + (1 - self.beta2) * np.power(gradi, 2)
                    for sdi, gradi in zip(sd, grads)]

        self.sdW = s_compute_running_average(self.sdW, self.dW)
        self.sdb = s_compute_running_average(self.sdb, self.db)

        def s_compute_correction(sd):
            return [sdi / (1 - np.power(self.beta2, self.t))
                    for sdi in sd]

        sdW_corrected = s_compute_correction(self.sdW)
        sdb_corrected = s_compute_correction(self.sdb)

        def update_weights(weights, v_corrected, s_corrected):
            return [Wi - self.learning_rate * v_corr / (np.sqrt(s_corr) + self.epsilon)
                    for Wi, v_corr, s_corr in zip(weights, v_corrected, s_corrected)]

        self.W = update_weights(self.W, vdW_corrected, sdW_corrected)
        self.b = update_weights(self.b, vdb_corrected, sdb_corrected)
        self.t = self.t + 1

    def check_inputs(self, inputs):
        """Check whether inputs have correct format"""
        if inputs.shape[0] != self.layer_spec[0]:
            raise RuntimeError("Inputs have incorrect shape!"
                               "Expected: {}"
                               "Received: {}".format(self.layer_spec[0],
                                                     inputs.shape[0]))

    def to_one_hot(self, labels):
        """Compute one-hot encoding of labels"""
        one_hot_labels = np.zeros((labels.max()+1, labels.shape[0]))
        for i, label in enumerate(labels):
            one_hot_labels[label, i] = 1
        return one_hot_labels

    def check_labels(self, labels):
        if isinstance(labels, list):
            labels = np.array(labels)

        if labels.shape[0] == self.layer_spec[-1]:
            return labels  # assume already one-hot

        labels = labels.flatten()  # otherwise, assume given class numbers 0 to num_classes - 1
        if labels.max() == self.layer_spec[-1] - 1:
            return self.to_one_hot(labels)

        raise RuntimeError("Training set labels in the wrong format!")

    def train(self, train_set, labels, mini_batch_size=100, num_epochs=10, shuffle=False):
        """
        Run full training loop
        :param train_set: The training set, 1 column per input
        :param labels: The training labels, either with class numbers from
           0 to max_num_classes-1, or one-hot encoded
        :param mini_batch_size: Mini batch size
        :param num_epochs: Number of epochs to run
        :param shuffle: Shuffle training set at each epoch?
        """

        self.check_inputs(train_set)
        labels = self.check_labels(labels)  # computes one-hot, if required

        self.train_init()  # initialise arrays needed for training

        np.random.seed(38)

        def split_into_batches(train_s):
            """Split the training set into batches using
            `np.split`. np.split requires the split to result
            in equal division, so we have to extract the last batch
            manually"""
            m = train_s.shape[1]  # number of samples
            last_batch_start = - (m % mini_batch_size)
            num_full_batches = (m + last_batch_start) / mini_batch_size

            if last_batch_start == 0:
                last_batch_start = m
            last_batch = train_s[:, last_batch_start:]  # could be empty, if m % mini_batch_size == 0

            batches = np.split(train_s[:, :last_batch_start], num_full_batches, axis=1)

            if last_batch.shape[1] > 0:  # ignore if empty
                batches.append(last_batch)
            return batches

        count = 0
        for epoch in range(num_epochs):

            if shuffle:
                rand_permutation = np.random.permutation(train_set.shape[1])
                train_set = train_set[:, rand_permutation]
                labels = labels[:, rand_permutation]  # shuffle differently for each epoch.

            train_batches = split_into_batches(train_set)
            labels_batches = split_into_batches(labels)

            for mini_batch, mini_batch_labels in zip(train_batches, labels_batches):
                self.forward(mini_batch, mini_batch_labels, dropout=True)
                self.backward(mini_batch_labels, dropout=True)
                self.gradient_update_adam()
                assert(not self.A and not self.Z and not self.D)

                count += 1
                if count % 500 == 0:
                    print("Current cost: {}".format(self.costs[-1]))

    def predict(self, inputs):
        """Predict by returning """
        self.check_inputs(inputs)
        activations, _ = self.forward(inputs)
        return np.argmax(activations, axis=0)

    def test(self, test_set, labels):
        """Compute accuracy given a test set"""
        self.check_inputs(test_set)
        self.check_labels(labels)
        if labels.shape[0] == test_set.shape[0]:  # then labels are already one-hot encoded, so convert to numeric
            labels = np.argmax(labels)

        predictions = self.predict(test_set)
        accuracy = (labels == predictions).mean()
        return accuracy


 def download_mnist(mnist_dir):
    """
    Download and gunzip the following files to `mnist_dir`:

    http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
    http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
    http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
    http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
    """
    import requests
    import gzip

    print("Downloading MNIST data")

    training_files = ('train-images-idx3-ubyte', 'train-labels-idx1-ubyte',
                     't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')
    url_home = 'http://yann.lecun.com/exdb/mnist/'
    for training_file in training_files:
        gzipped_file = training_file + ".gz"
        print("Downloading and unzipping {}".format(gzipped_file))

        url = url_home + gzipped_file
        resp = requests.get(url)  # download

        path_gzipped = os.path.join(mnist_dir, gzipped_file)
        with open(path_gzipped, "wb") as fh:
            fh.write(resp.content)  # save to disk

        path_file = os.path.join(mnist_dir, training_file)
        with gzip.open(path_gzipped, 'rb') as fh_gzipped:
            with open(path_file, 'wb') as fh:
                data = fh_gzipped.read()  # gunzip
                fh.write(data)  # saved gunzipped version to disk


 def load_mnist(mnist_dir):
    """Method to load MNIST data from filesystem"""

    def load_images(path_images):
        with open(path_images, 'rb') as fh:
            header_buf = fh.read(4 * 4)  # first 4 integers are a header
            header = np.frombuffer(header_buf, dtype='>i4')  # read big endian integers ('>i4')
            assert (header[0] == 2051)  # check that the magic number is correct
            assert (header[1] == 60000 or header[1] == 10000)  # set should have either 60000 or 10000 images (train & test, respectively)
            assert (header[2] == 28)  # check image width is 28
            assert (header[3] == 28)  # check image height is 28
            images_buf = fh.read()
            images = np.frombuffer(images_buf, dtype=np.uint8).reshape((-1, 28*28)).T
            images_normalized = images / 255.
            return images_normalized

    def load_labels(path_labels):
        with open(path_labels, 'rb') as fh:
            header_buf = fh.read(2 * 4)  # first 2 integers are a header
            header = np.frombuffer(header_buf, dtype='>i4')
            assert (header[0] == 2049)  # check that the magic number is correct
            assert (header[1] == 60000 or header[1] == 10000)
            labels_buf = fh.read()
            labels = np.frombuffer(labels_buf, dtype=np.uint8)
            return labels

    train_images = load_images(os.path.join(mnist_dir, "train-images-idx3-ubyte"))
    train_labels = load_labels(os.path.join(mnist_dir, "train-labels-idx1-ubyte"))
    test_images = load_images(os.path.join(mnist_dir, "t10k-images-idx3-ubyte"))
    test_labels = load_labels(os.path.join(mnist_dir, "t10k-labels-idx1-ubyte"))
    return train_images, train_labels, test_images, test_labels


 def show_images(images, labels):
    """Plot the training data and its labels to check everything is OK"""
    plt.title("Showing some training images and their labels")
    count = 1
    for image_ind in [34, 2302, 34598]:
        image = images[:, image_ind].reshape(28, 28)
        ax = plt.subplot(130 + count)
        ax.set_title("Should be {}".format(labels[image_ind]))
        plt.imshow(image)
        count += 1

    plt.suptitle("""Showing some training images and their labels
 Check whether the labels are accurate! If not, something's wrong!
 Close the window to start training.""")
    plt.show()


 def check_with_small_net():
    """Creates a small net with random inputs
    to check backprop using gradient checking"""
    net = FullyConnectedNet([100, 25, 10, 2])
    net.train_init()
    inputs = np.random.rand(100, 1)
    labels = np.array([1, 0]).reshape(2, 1)
    net.check_gradient(inputs, labels)


 def main():
    check_with_small_net()  # check that backprop is implemented correctly

    import argparse
    parser = argparse.ArgumentParser(description="""
 Train a fully connected NN to classify MNIST digits.

 Example usage:

 python fully_connected_net.py --mnist_dir mnist_data --layer_spec 784 200 10

 This trains the mnist data in `mnist_data` using a fully connected net with
 input layer  of size 784, hidden layer of size 200, and output layer of size 10

 If the data is not already in `mnist_data`, it will be downloaded
 """)

    parser.add_argument("--mnist_dir", help="Directory to download mnist data. "
                                            "If it doesn't exist, the data will be downloaded",
                        default='mnist-data')
    parser.add_argument("--layer_spec", help="""The structure of the net, a sequence of integers.
 The first element MUST be 784 and the last element MUST be 10""", type=int, nargs='+')
    parser.add_argument("--num_epochs", help="Number of epochs to train", type=int, default=10)

    args = parser.parse_args()

    MNIST_INPUT = 784
    MNIST_OUTPUT = 10

    layer_spec = [MNIST_INPUT, 1000, 100, 30, MNIST_OUTPUT]  # 4-layer NN
    if args.layer_spec:
        layer_spec = args.layer_spec
        if layer_spec[0] != MNIST_INPUT:
            raise RuntimeError("Input layer must have dimension {}".format(MNIST_INPUT))
        if layer_spec[-1] != MNIST_OUTPUT:
            raise RuntimeError("Output layer must have dimension {}".format(MNIST_OUTPUT))

    print("Layer spec: {}".format(layer_spec))
    net = FullyConnectedNet(layer_spec)

    print("MNIST directory: {}".format(args.mnist_dir))
    if not os.path.isdir(args.mnist_dir):
        os.mkdir(args.mnist_dir)
        download_mnist(args.mnist_dir)

    train_images, train_labels, test_images, test_labels = load_mnist(args.mnist_dir)

    show_images(train_images, train_labels)

    # train and plot decreasing cost
    print("About to train {} epochs".format(args.num_epochs))
    net.train(train_images, train_labels, shuffle=True, num_epochs=args.num_epochs)

    print("Plotting cost per iteration")
    plt.plot(np.array(net.costs))
    plt.ylabel('Cross-entropy cost')
    plt.xlabel('Iteration number')
    plt.title("Cost per iteration")
    plt.show()

    train_accuracy = net.test(train_images, train_labels)
    print("Train accuracy: {:.2f}%".format(100*train_accuracy))

    test_accuracy = net.test(test_images, test_labels)
    print("Test accuracy: {:.2f}%".format(100*test_accuracy))


 if __name__ == "__main__":
    main()
	"""
	Class to define a fully connected neural net. Written using
	numpy only.

	The number and size of hidden layers is defined by providing
	a list to the constructor, for example

	```
	net = FullyConnectedNet([1024, 450, 10])
	```

	In this case, `net` represents a 3-layer neural net, with an input
	unit of size 1024, a hidden unit of size 450, and an output
	unit of size 10.

	The following defaults are hardcoded (for now):

	- The hidden layers have RELU activation
	- The output layer has SOFTMAX activation
	- The cost function is the cross-entropy cost
	- Weights use He initialisation
	- The update optimizer rule is ADAM (but you can choose the parameters)
	- Dropout regularization is used (but you can choose the parameter)

	Also implements gradient checking to check that backprop is
	working correctly.
	"""
	import os

	import numpy as np
	import matplotlib.pyplot as plt


	class FullyConnectedNet:

	def __init__(self, layer_spec, dropout_prob=0.25, learning_rate=0.003,
	beta1=0.9, beta2=0.999, epsilon=1e-8):
	"""Initialise learning hyperparameters"""
	self.layer_spec = layer_spec
	self.dropout_prob = dropout_prob
	self.learning_rate = learning_rate
	self.beta1 = beta1
	self.beta2 = beta2
	self.epsilon = epsilon

	def train_init(self):
	"""Initialise all the parameters I require for training"""
	self.bias_init()
	self.weight_init_he()
	self.adam_init()

	self.A, self.Z, self.D, self.costs = [], [], [], []
	self.dW, self.db = [], []

	def bias_init(self):
	"""Initialise biases to 0"""
	self.b = [np.zeros((sz_layer, 1)) for sz_layer in self.layer_spec[1:]]

	def weight_init_he(self):
	"""Initialise weights using He initialisation"""
	np.random.seed(8)
	self.W = [np.random.randn(self.layer_spec[i], self.layer_spec[i - 1]) * np.sqrt(2 / self.layer_spec[i - 1])
	for i in range(1, len(self.layer_spec))]

	def weight_init_random(self):
	"""Random initialisation of weights, used for debugging"""
	np.random.seed(3)
	self.W = [np.random.randn(self.layer_spec[i], self.layer_spec[i - 1]) * 0.01
	for i in range(1, len(self.layer_spec))]

	def weight_init_zeros(self):
	"""Zeros initialisation of weights, used for debugging"""
	self.W = [np.zeros((self.layer_spec[i], self.layer_spec[i - 1]))
	for i in range(1, len(self.layer_spec))]
	# only used for debugging!

	def adam_init(self):
	"""Initialise adam moving averages"""
	self.t = 1
	self.vdW = [np.zeros(Wi.shape) for Wi in self.W]
	self.vdb = [np.zeros(bi.shape) for bi in self.b]
	self.sdW = [np.zeros(Wi.shape) for Wi in self.W]
	self.sdb = [np.zeros(bi.shape) for bi in self.b]

	def relu(self, x):
	return np.maximum(x, 0)

	def softmax(self, z):
	y = np.exp(z)
	y_soft = y / y.sum(axis=0)
	return y_soft

	def cross_entropy_cost(self, y_soft, labels):
	assert (y_soft.shape == labels.shape) # labels need to be 1-hot encoded
	m = labels.shape[1] # number of samples
	cost = -np.multiply(np.log(y_soft), labels).sum() / m
	return cost

	def relu_grad(self, x):
	return x > 0

	def cross_entropy_with_softmax_grad(self, y_soft, labels):
	"""Backprops directly to Z's"""
	grad = y_soft - labels
	return grad

	def forward(self, batch, labels=None, dropout=False):
	"""
	Do a full forward pass over all layers, for a batch of inputs.
	Computes and stores the cost in `self.costs`
	:param batch: The batch, dimensions: dim_input x num_samples
	:param labels: The labels, 1-hot encoded
	:param dropout: `True` in order to use dropout
	"""

	def single_forward(prev_layer, weights, bias, activation_func, with_dropout=False):
	"""Given activations of one layer, do a forward pass and
	compute the activations of the next"""
	Z = np.dot(weights, prev_layer) + bias
	A = activation_func(Z)

	if with_dropout:
	D = np.random.rand(*A.shape) > self.dropout_prob
	A = np.multiply(A, D) # apply dropout mask

	self.Z.append(Z)
	self.A.append(A)
	if with_dropout:
	self.D.append(D)

	self.A.append(batch) # populate initial 'activations'
	for Wi, bi in zip(self.W[:-1], self.b[:-1]):
	Aprev = self.A[-1]
	single_forward(Aprev, Wi, bi, self.relu, with_dropout=dropout)
	# compute RELU activation for all units

	single_forward(self.A[-1], self.W[-1], self.b[-1], self.softmax)
	# compute sigmoid activation for final layer

	cost = None
	if labels is not None:
	cost = self.cross_entropy_cost(self.A[-1], labels)
	self.costs.append(cost) # keep track of costs, for debugging and checking

	return self.A[-1], cost # return final activations and cost

	def backward(self, labels, dropout=False):
	"""
	Do a full backward pass over all layers.
	:param labels: The labels used for forward propagation, one-hot encoded.
	"""
	self.dW, self.db = [], [] # reset weights
	m = labels.shape[1] # number of samples in batch

	def single_backward(dA, weights, with_dropout=False, labels=None):
	"""Do a backward pass for a single layer.
	The case `labels is not None` is used for last layer, instead of dA.
	Otherwise, relu_grad is used"""
	if with_dropout and dA is not None:
	D = self.D.pop()
	dA = np.multiply(dA, D) # apply previously created dropout mask to gradient.

	if labels is not None:
	dZ = self.cross_entropy_with_softmax_grad(self.A.pop(), labels) # gradient for last layer
	self.Z.pop() # final 'Z' not required, throw it away
	else:
	dZ = np.multiply(self.relu_grad(self.Z.pop()), dA)
	dW = np.dot(dZ, self.A.pop().T) / m
	db = np.sum(dZ, axis=1, keepdims=True) / m
	self.dW.insert(0, dW)
	self.db.insert(0, db)
	return np.dot(weights.T, dZ) # return updated dA

	dA_current = single_backward(None, self.W[-1], labels=labels)
	# do first backward pass in case 'softmax'

	for Wi in reversed(self.W[:-1]):
	dA_current = single_backward(dA_current, Wi, with_dropout=dropout)
	# do all 'RELU' backward passes

	def check_gradient(self, batch, labels, eps=1e-4, check_thresh=1e-6):
	"""Method implementing gradient checking:
	for each example in the batch, compute dW by backprop
	and manually, by computing the partial derivative using
	partial approx= (cost(x+eps) - cost(x-eps)) / 2*eps.

	Return the maximum difference of gradients"""

	self.forward(batch, labels)
	self.backward(labels) # computes dW

	max_diff = 0.0

	for Wi, dWi in zip(self.W, self.dW):
	dWi_manual = np.zeros(Wi.shape)
	for iter_W in range(len(dWi_manual.flat)):
	Wi.flat[iter_W] += eps
	_, cost_plus = self.forward(batch, labels)
	Wi.flat[iter_W] -= 2*eps
	_, cost_minus = self.forward(batch, labels)
	Wi.flat[iter_W] += eps
	dWi_manual.flat[iter_W] = (cost_plus - cost_minus) / (2*eps)

	max_diff = max(max_diff, abs(dWi_manual - dWi).max())

	if max_diff > check_thresh:
	raise RuntimeError("Net not passing gradient checking, max_diff = {:.4f}. "
	"Something in the code is probably wrong!".format(max_diff))

	def gradient_update_simple(self):
	"""Simple gradient update, for debugging"""
	self.W = [Wi - self.learning_rate * dWi
	for Wi, dWi in zip(self.W, self.dW)]
	self.b = [bi - self.learning_rate * dbi
	for bi, dbi in zip(self.b, self.db)]

	def gradient_update_adam(self):
	"""Use stored gradients dW to perform an ADAM update of
	the weights W"""

	def v_compute_running_average(vd, grads):
	return [self.beta1 * vdi + (1 - self.beta1) * gradi
	for vdi, gradi in zip(vd, grads)]

	self.vdW = v_compute_running_average(self.vdW, self.dW)
	self.vdb = v_compute_running_average(self.vdb, self.db)

	def v_compute_correction(vd):
	return [vdi / (1 - np.power(self.beta1, self.t))
	for vdi in vd]

	vdW_corrected = v_compute_correction(self.vdW)
	vdb_corrected = v_compute_correction(self.vdb)

	def s_compute_running_average(sd, grads):
	return [self.beta2 * sdi + (1 - self.beta2) * np.power(gradi, 2)
	for sdi, gradi in zip(sd, grads)]

	self.sdW = s_compute_running_average(self.sdW, self.dW)
	self.sdb = s_compute_running_average(self.sdb, self.db)

	def s_compute_correction(sd):
	return [sdi / (1 - np.power(self.beta2, self.t))
	for sdi in sd]

	sdW_corrected = s_compute_correction(self.sdW)
	sdb_corrected = s_compute_correction(self.sdb)

	def update_weights(weights, v_corrected, s_corrected):
	return [Wi - self.learning_rate * v_corr / (np.sqrt(s_corr) + self.epsilon)
	for Wi, v_corr, s_corr in zip(weights, v_corrected, s_corrected)]

	self.W = update_weights(self.W, vdW_corrected, sdW_corrected)
	self.b = update_weights(self.b, vdb_corrected, sdb_corrected)
	self.t = self.t + 1

	def check_inputs(self, inputs):
	"""Check whether inputs have correct format"""
	if inputs.shape[0] != self.layer_spec[0]:
	raise RuntimeError("Inputs have incorrect shape!"
	"Expected: {}"
	"Received: {}".format(self.layer_spec[0],
	inputs.shape[0]))

	def to_one_hot(self, labels):
	"""Compute one-hot encoding of labels"""
	one_hot_labels = np.zeros((labels.max()+1, labels.shape[0]))
	for i, label in enumerate(labels):
	one_hot_labels[label, i] = 1
	return one_hot_labels

	def check_labels(self, labels):
	if isinstance(labels, list):
	labels = np.array(labels)

	if labels.shape[0] == self.layer_spec[-1]:
	return labels # assume already one-hot

	labels = labels.flatten() # otherwise, assume given class numbers 0 to num_classes - 1
	if labels.max() == self.layer_spec[-1] - 1:
	return self.to_one_hot(labels)

	raise RuntimeError("Training set labels in the wrong format!")

	def train(self, train_set, labels, mini_batch_size=100, num_epochs=10, shuffle=False):
	"""
	Run full training loop
	:param train_set: The training set, 1 column per input
	:param labels: The training labels, either with class numbers from
	0 to max_num_classes-1, or one-hot encoded
	:param mini_batch_size: Mini batch size
	:param num_epochs: Number of epochs to run
	:param shuffle: Shuffle training set at each epoch?
	"""

	self.check_inputs(train_set)
	labels = self.check_labels(labels) # computes one-hot, if required

	self.train_init() # initialise arrays needed for training

	np.random.seed(38)

	def split_into_batches(train_s):
	"""Split the training set into batches using
	`np.split`. np.split requires the split to result
	in equal division, so we have to extract the last batch
	manually"""
	m = train_s.shape[1] # number of samples
	last_batch_start = - (m % mini_batch_size)
	num_full_batches = (m + last_batch_start) / mini_batch_size

	if last_batch_start == 0:
	last_batch_start = m
	last_batch = train_s[:, last_batch_start:] # could be empty, if m % mini_batch_size == 0

	batches = np.split(train_s[:, :last_batch_start], num_full_batches, axis=1)

	if last_batch.shape[1] > 0: # ignore if empty
	batches.append(last_batch)
	return batches

	count = 0
	for epoch in range(num_epochs):

	if shuffle:
	rand_permutation = np.random.permutation(train_set.shape[1])
	train_set = train_set[:, rand_permutation]
	labels = labels[:, rand_permutation] # shuffle differently for each epoch.

	train_batches = split_into_batches(train_set)
	labels_batches = split_into_batches(labels)

	for mini_batch, mini_batch_labels in zip(train_batches, labels_batches):
	self.forward(mini_batch, mini_batch_labels, dropout=True)
	self.backward(mini_batch_labels, dropout=True)
	self.gradient_update_adam()
	assert(not self.A and not self.Z and not self.D)

	count += 1
	if count % 500 == 0:
	print("Current cost: {}".format(self.costs[-1]))

	def predict(self, inputs):
	"""Predict by returning """
	self.check_inputs(inputs)
	activations, _ = self.forward(inputs)
	return np.argmax(activations, axis=0)

	def test(self, test_set, labels):
	"""Compute accuracy given a test set"""
	self.check_inputs(test_set)
	self.check_labels(labels)
	if labels.shape[0] == test_set.shape[0]: # then labels are already one-hot encoded, so convert to numeric
	labels = np.argmax(labels)

	predictions = self.predict(test_set)
	accuracy = (labels == predictions).mean()
	return accuracy


	def download_mnist(mnist_dir):
	"""
	Download and gunzip the following files to `mnist_dir`:

	http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
	http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
	http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
	http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
	"""
	import requests
	import gzip

	print("Downloading MNIST data")

	training_files = ('train-images-idx3-ubyte', 'train-labels-idx1-ubyte',
	't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')
	url_home = 'http://yann.lecun.com/exdb/mnist/'
	for training_file in training_files:
	gzipped_file = training_file + ".gz"
	print("Downloading and unzipping {}".format(gzipped_file))

	url = url_home + gzipped_file
	resp = requests.get(url) # download

	path_gzipped = os.path.join(mnist_dir, gzipped_file)
	with open(path_gzipped, "wb") as fh:
	fh.write(resp.content) # save to disk

	path_file = os.path.join(mnist_dir, training_file)
	with gzip.open(path_gzipped, 'rb') as fh_gzipped:
	with open(path_file, 'wb') as fh:
	data = fh_gzipped.read() # gunzip
	fh.write(data) # saved gunzipped version to disk


	def load_mnist(mnist_dir):
	"""Method to load MNIST data from filesystem"""

	def load_images(path_images):
	with open(path_images, 'rb') as fh:
	header_buf = fh.read(4 * 4) # first 4 integers are a header
	header = np.frombuffer(header_buf, dtype='>i4') # read big endian integers ('>i4')
	assert (header[0] == 2051) # check that the magic number is correct
	assert (header[1] == 60000 or header[1] == 10000) # set should have either 60000 or 10000 images (train & test, respectively)
	assert (header[2] == 28) # check image width is 28
	assert (header[3] == 28) # check image height is 28
	images_buf = fh.read()
	images = np.frombuffer(images_buf, dtype=np.uint8).reshape((-1, 28*28)).T
	images_normalized = images / 255.
	return images_normalized

	def load_labels(path_labels):
	with open(path_labels, 'rb') as fh:
	header_buf = fh.read(2 * 4) # first 2 integers are a header
	header = np.frombuffer(header_buf, dtype='>i4')
	assert (header[0] == 2049) # check that the magic number is correct
	assert (header[1] == 60000 or header[1] == 10000)
	labels_buf = fh.read()
	labels = np.frombuffer(labels_buf, dtype=np.uint8)
	return labels

	train_images = load_images(os.path.join(mnist_dir, "train-images-idx3-ubyte"))
	train_labels = load_labels(os.path.join(mnist_dir, "train-labels-idx1-ubyte"))
	test_images = load_images(os.path.join(mnist_dir, "t10k-images-idx3-ubyte"))
	test_labels = load_labels(os.path.join(mnist_dir, "t10k-labels-idx1-ubyte"))
	return train_images, train_labels, test_images, test_labels


	def show_images(images, labels):
	"""Plot the training data and its labels to check everything is OK"""
	plt.title("Showing some training images and their labels")
	count = 1
	for image_ind in [34, 2302, 34598]:
	image = images[:, image_ind].reshape(28, 28)
	ax = plt.subplot(130 + count)
	ax.set_title("Should be {}".format(labels[image_ind]))
	plt.imshow(image)
	count += 1

	plt.suptitle("""Showing some training images and their labels
	Check whether the labels are accurate! If not, something's wrong!
	Close the window to start training.""")
	plt.show()


	def check_with_small_net():
	"""Creates a small net with random inputs
	to check backprop using gradient checking"""
	net = FullyConnectedNet([100, 25, 10, 2])
	net.train_init()
	inputs = np.random.rand(100, 1)
	labels = np.array([1, 0]).reshape(2, 1)
	net.check_gradient(inputs, labels)


	def main():
	check_with_small_net() # check that backprop is implemented correctly

	import argparse
	parser = argparse.ArgumentParser(description="""
	Train a fully connected NN to classify MNIST digits.

	Example usage:

	python fully_connected_net.py --mnist_dir mnist_data --layer_spec 784 200 10

	This trains the mnist data in `mnist_data` using a fully connected net with
	input layer of size 784, hidden layer of size 200, and output layer of size 10

	If the data is not already in `mnist_data`, it will be downloaded
	""")

	parser.add_argument("--mnist_dir", help="Directory to download mnist data. "
	"If it doesn't exist, the data will be downloaded",
	default='mnist-data')
	parser.add_argument("--layer_spec", help="""The structure of the net, a sequence of integers.
	The first element MUST be 784 and the last element MUST be 10""", type=int, nargs='+')
	parser.add_argument("--num_epochs", help="Number of epochs to train", type=int, default=10)

	args = parser.parse_args()

	MNIST_INPUT = 784
	MNIST_OUTPUT = 10

	layer_spec = [MNIST_INPUT, 1000, 100, 30, MNIST_OUTPUT] # 4-layer NN
	if args.layer_spec:
	layer_spec = args.layer_spec
	if layer_spec[0] != MNIST_INPUT:
	raise RuntimeError("Input layer must have dimension {}".format(MNIST_INPUT))
	if layer_spec[-1] != MNIST_OUTPUT:
	raise RuntimeError("Output layer must have dimension {}".format(MNIST_OUTPUT))

	print("Layer spec: {}".format(layer_spec))
	net = FullyConnectedNet(layer_spec)

	print("MNIST directory: {}".format(args.mnist_dir))
	if not os.path.isdir(args.mnist_dir):
	os.mkdir(args.mnist_dir)
	download_mnist(args.mnist_dir)

	train_images, train_labels, test_images, test_labels = load_mnist(args.mnist_dir)

	show_images(train_images, train_labels)

	# train and plot decreasing cost
	print("About to train {} epochs".format(args.num_epochs))
	net.train(train_images, train_labels, shuffle=True, num_epochs=args.num_epochs)

	print("Plotting cost per iteration")
	plt.plot(np.array(net.costs))
	plt.ylabel('Cross-entropy cost')
	plt.xlabel('Iteration number')
	plt.title("Cost per iteration")
	plt.show()

	train_accuracy = net.test(train_images, train_labels)
	print("Train accuracy: {:.2f}%".format(100*train_accuracy))

	test_accuracy = net.test(test_images, test_labels)
	print("Test accuracy: {:.2f}%".format(100*test_accuracy))


	if __name__ == "__main__":
	main()