From 95056e0afb6b640c382e4b471bce95edcbe9c30d Mon Sep 17 00:00:00 2001 From: Dinesh Natesan Date: Thu, 29 Dec 2016 16:12:01 +0530 Subject: [PATCH] moving pkgs to packages and adding libraries --- .gitignore | 6 +- ...ulti-layer-perceptron-implementation.ipynb | 66 +++- libraries/{ANN/__init__.py => ANN.py} | 0 packages/ANN/__init__.py | 320 ++++++++++++++++++ {libraries => packages}/LICENSE | 0 {libraries => packages}/MANIFEST.in | 0 {libraries => packages}/README.rst | 0 {libraries => packages}/TODO | 0 .../dev/test_script_XOR-dev.ipynb | 0 .../examples/notebooks/test_script_XOR.ipynb | 0 .../examples/notebooks/test_script_iris.ipynb | 0 {libraries => packages}/examples/test_data.py | 0 .../examples/test_script.py | 0 {libraries => packages}/setup.py | 0 14 files changed, 380 insertions(+), 12 deletions(-) rename libraries/{ANN/__init__.py => ANN.py} (100%) create mode 100644 packages/ANN/__init__.py rename {libraries => packages}/LICENSE (100%) rename {libraries => packages}/MANIFEST.in (100%) rename {libraries => packages}/README.rst (100%) rename {libraries => packages}/TODO (100%) rename {libraries => packages}/dev/test_script_XOR-dev.ipynb (100%) rename {libraries => packages}/examples/notebooks/test_script_XOR.ipynb (100%) rename {libraries => packages}/examples/notebooks/test_script_iris.ipynb (100%) rename {libraries => packages}/examples/test_data.py (100%) rename {libraries => packages}/examples/test_script.py (100%) rename {libraries => packages}/setup.py (100%) diff --git a/.gitignore b/.gitignore index 6c790f8..4cb093d 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,6 @@ Compiled python modules. *.pyc # Setuptools distribution folder. -libraries/dist/ -libraries/build/ -libraries/*.egg-info +packages/dist/ +packages/build/ +packages/*.egg-info diff --git a/03-Multi-layer-perceptron-implementation.ipynb b/03-Multi-layer-perceptron-implementation.ipynb index a7655c2..7c2d2a2 100644 --- a/03-Multi-layer-perceptron-implementation.ipynb +++ b/03-Multi-layer-perceptron-implementation.ipynb @@ -26,6 +26,13 @@ "from IPython import display" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize the multilayer perceptron" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -51,9 +58,7 @@ "iterations = 5000\n", "eta = 0.3\n", "\n", - "nn1 = ANN.FNN(numLayers, Input, target, eta=eta)\n", - "\n", - "#output, error = nn1.train(iterations)" + "nn1 = ANN.FNN(numLayers, Input, target, eta=eta)\n" ] }, { @@ -65,11 +70,19 @@ "outputs": [], "source": [ "target = nn1.__target__\n", - "\n", "error = []\n", "output = []" ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Plot expected classification" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -81,7 +94,7 @@ "data": { "image/png": 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7knwiyc8muV98iA8AAOylJJYBgL3WO9/09bzzTd+4wtg3v7G4u8tVkl7lsccm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\nCeOdz69/HP374CRfGPW5Z4bX0+d2E5LP+AAA+6YNub6rXuW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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -110,6 +123,13 @@ "f.canvas.draw()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn and classify XOR data" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -121,7 +141,7 @@ "data": { "image/png": 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7knwiyc8muV98iA8AAOylJJYBgL3WO9/09bzzTd+4wtg3v7G4u8tVkl7lsccm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\nCeOdz69/HP374CRfGPW5Z4bX0+d2E5LP+AAA+6YNub6rXuW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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -157,6 +177,13 @@ "# IRIS Dataset" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load dataset" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -170,6 +197,13 @@ "target = data.target" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connect up the multilayer perceptron" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -210,6 +244,13 @@ "output = []" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot expected classification" + ] + }, { "cell_type": "code", "execution_count": 9, @@ -221,7 +262,7 @@ "data": { "image/png": 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Gh5y8o6pekeSjSa6X5M4Zbi88OMPF22rGAgCAddPdn6uqRyR5bZJzxmv1T2R4QPo9kjw0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\ny3Ch/fF1CnVdjF2Q3j3Jcd195YImW6YvMsxTd9/c/J16vwwPPHplhgd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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -248,6 +289,13 @@ "f.canvas.draw()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn, classify and visualize classifications" + ] + }, { "cell_type": "code", "execution_count": 10, @@ -257,9 +305,9 @@ "outputs": [ { "data": { - "image/png": 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HTScFD//IrNe1si8JPzu5vKrvPZrqe52rZiarp9beczq8/teya1aCv/9juWrtzGO7tk2v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SmvelPO9Nz4uUOroCVnWIiOnA49Pe9am3Fk90Qcmy1+qrC0pueZjRgpKv7lldC0q2O38f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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/libraries/ANN/__init__.py b/libraries/ANN.py similarity index 100% rename from libraries/ANN/__init__.py rename to libraries/ANN.py diff --git a/packages/ANN/__init__.py b/packages/ANN/__init__.py new file mode 100644 index 0000000..c0b42ae --- /dev/null +++ b/packages/ANN/__init__.py @@ -0,0 +1,320 @@ +import numpy as np + + +class FNN: + ''' This is an artificial neural network class. + It learns using the backpropagation algorithm, and can classify binary + as well as multi-class problems. At the moment it can only + run in batch learning mode.''' + + def __init__(self, numLayers, Input, target, hiddenNeuronList=[], eta=0.1, + mode='batch', error_function='quadratic'): + ''' Initialize an instance of the machine learning class ''' + self.mode = mode + self.numLayers = numLayers + self.numHiddenLayers = numLayers - 2 + self.eta = eta + self.error_function = error_function + self.__Input__ = np.matrix(Input).T + + self.number_of_features = self.__Input__.shape[0] + self.number_of_training_points = self.__Input__.shape[1] + + self.__Input__ = np.vstack( + [self.__Input__, [1]*self.number_of_training_points]) # Add bias + self.class_labels = set(target) + + self.number_of_classes = len(self.class_labels) + print("Class labels:{}".format(self.class_labels)) + self.set_target(target) + + if not len(hiddenNeuronList): + # Should be changed later to something more general + self.hiddenNeuronList = [self.number_of_features] * \ + self.numHiddenLayers + else: + self.hiddenNeuronList = hiddenNeuronList + + self.construct_network() + print("Network constructed with {} layers, learning rate is {}" + .format(self.numLayers, self.eta)) + self.connect_layers() + print("Layers connected") + + # Neural network construction methods + def construct_network(self): + ''' Construct the different layers and units of the NN ''' + # Input layer Stuff + self.input_layer = input_layer(self.number_of_features) + + # Create Hidden Layers + self.hidden_layers = [ + hidden_layer(self.hiddenNeuronList[i], + self.number_of_training_points, self.eta) + for i in range(self.numHiddenLayers)] + + # Create output layer + self.output_layer = output_layer( + self.number_of_classes, self.number_of_training_points, self.eta) + + self.layers = [self.input_layer] + self.hidden_layers + \ + [self.output_layer] + + def connect_layers(self): + '''Connect layers''' + # Input layer + self.hidden_layers[0].connect_layer(self.input_layer) + # Hidden layers + for n in range(self.numHiddenLayers-1): + self.hidden_layers[n+1].connect_layer(self.hidden_layers[n]) + # Output layer + self.output_layer.connect_layer(self.hidden_layers[-1]) + + def set_target(self, target): + ''' Setting target to the ANN''' + try: + np.shape(self.__Input__)[0] == len(target) + + if self.number_of_classes > 2: # More than binary classification + self.__target__ = np.zeros( # Expected output from each neuron + (self.number_of_classes, self.number_of_training_points)) + for i, label in enumerate(self.class_labels): + for j, t in enumerate(target): + if label == t: + self.__target__[i, j] = 1 + else: + self.__target__ = np.zeros((1, self.number_of_training_points)) + self.__target__[0] = target + + except: + return "Lengths of input and target don't match" + + # Cost functions for the NN + def calculateError(self, t, o): + '''This is the main error/cost function''' + if self.error_function == 'quadratic': + return self.quadratic(t, o) + + def quadratic(self, t, o): + ''' This is quadratic cost function ''' + return (1./2)*(np.sum(np.square(t-o))) + + # The learning rule and weights updates ## + def backpropagate(self, target): + ''' Backpropagation of errors through the NN ''' + self.output_layer.backpropagate(target) + for layer in self.hidden_layers[::-1]: + layer.backpropagate() + + def update_weights(self): + ''' NN weight updates ''' + for layer in self.layers[1:]: + layer.update() + + # Prediction related methods #### + + def compute_forward(self, input): + '''Forward computation by NN by passing through activation function''' + self.input_layer.compute_layer(input) + for layer in self.hidden_layers: + layer.compute_layer() + self.pred_class = self.output_layer.compute_layer() + + def train(self, iterations=1): + ''' This is the main iteration function which forward computes, + backpropagates, and updates weights for the NN ''' + error = [] + for i in range(iterations): + self.compute_forward(self.__Input__) + self.backpropagate(self.__target__) + self.update_weights() + error.append( + self.calculateError( + self.__target__, + self.output_layer.output)) + #if i % (iterations/10.) == 0.: + # print("{} iterations, loss = {}".format(i+1, error[-1])) + if iterations == 1: + return self.output_layer.output, error[0] + else: + return self.output_layer.output, error + + def test(self, test_data): + ''' This is the main function which forward computes + and classifies test data ''' + self.compute_forward(test_data) + return self.pred_class + + +class neuron_layer: + ''' This is a neural network layer class''' + + def __init__(self, N, numDataPoints, eta): + ''' This initializes a neural network layer ''' + if isinstance(self, hidden_layer): + self.N = N+1 # Adding bias neurons to the hidden layers + else: + if N == 2: # Special provision for binary classification + self.N = 1 + else: + self.N = N + self.neurons = [neuron(self, index) for index in range(self.N)] + self.eta = eta + self.output = np.zeros((self.N, numDataPoints)) + self.delta = np.zeros((self.N, numDataPoints)) + + def connect_layer(self, prev_layer): + ''' This connects neural network layers together ''' + self.prev_layer = prev_layer + self.index = self.prev_layer.index + 1 + prev_layer.set_next_layer(self) + for n in self.neurons: + n.initialize_weights(prev_layer.N) + + def compute_layer(self): + ''' Compute activation for all neurons in layer ''' + for i, n in enumerate(self.neurons): + self.output[i] = n.compute() + n.set_w_out() # Setting output weights + return self.output + + def update(self): + ''' Update weights for all neurons in layer ''' + for i, neuron in enumerate(self.neurons): + neuron.change_weight(self.eta) + + +class input_layer(neuron_layer): + ''' This is the input layer class''' + + def __init__(self, N): + self.N = N + 1 + self.index = 0 + + def compute_layer(self, x): + self.output = x + return self.output + + def set_next_layer(self, next_layer): + self.next_layer = next_layer + + +class hidden_layer(neuron_layer): + ''' This is the hidden layer class''' + + def set_next_layer(self, next_layer): + self.next_layer = next_layer + + def backpropagate(self): + next_delta = self.next_layer.delta + # print neuron.w_out, next_delta + for i, neuron in enumerate(self.neurons): + self.delta[i] = neuron.set_delta(neuron.d_activation * + np.dot(neuron.w_out, next_delta)) + + +class output_layer(neuron_layer): + ''' This is the output layer class''' + + def backpropagate(self, target): + for i, neuron in enumerate(self.neurons): + self.delta[i] = neuron.set_delta( + (target[i] - neuron.output) * + neuron.d_activation) + + +class neuron: + '''This is a neuron (Units inside a layer) class''' + + def __init__(self, layer, index, + activation_method='sigmoid', bias_constant=0.99): + ''' Initialize a neuron instance ''' + self.layer = layer + self.index = index + self.activation_method = activation_method + self.bias_constant = bias_constant + + def initialize_weights(self, numInputs): + ''' Randomly assign initial weights from a uniform distribution ''' + self.w = np.random.uniform(-1, 1, numInputs) + # self.w = np.zeros(numInputs) # Just for kicks + + def set_w_out(self): + ''' Get all weights going out of the neuron ''' + if isinstance(self.layer, output_layer): + self.w_out = None + elif isinstance(self.layer, hidden_layer): + w_out = [n.w[self.index] for n in self.layer.next_layer.neurons] + self.w_out = np.array(w_out) + + def compute(self): + ''' Compute the activation output for regular and bias neurons ''' + if not (isinstance(self.layer, hidden_layer) and self.index == 0): + input = np.ravel(np.dot(np.transpose(self.w), + self.layer.prev_layer.output)) + self.output = self.activation(input) + self.d_activation = self.activation_diff(self.output) + else: + factor = self.bias_constant + # Bias units outputing constants all the time. + self.output = np.ones(self.layer.prev_layer.output.shape[1]) \ + * factor + self.d_activation = self.activation_diff(self.output) + return self.output + + def set_delta(self, delta): + self.delta = delta + return self.delta + + def change_weight(self, eta): + ''' Update weights for neuron ''' + # Seems to work right. Check this once. + self.w += eta * np.ravel(np.dot(self.delta, + self.layer.prev_layer.output.T)) + + # Activation functions # + def activation(self, input): + ''' This is our activation function. ''' + if self.activation_method == 'sigmoid': + return self.sigmoid(input) + elif self.activation_method == 'tanh': + return self.tanh(input) + + def activation_diff(self, x): + ''' This is our activation derivative function. ''' + if self.activation_method == 'sigmoid': + return self.sigmoid_diff(x) + elif self.activation_method == 'tanh': + return self.tanh_diff(x) + + # Sigmoid activation # + def sigmoid(self, x): + ''' This is sigmoid activation function. ''' + return 1/(1+np.exp(-x)) + + def sigmoid_diff(self, output): + ''' This is derivative of the sigmoid activation function. ''' + return output*(1-output) + + # Hyperbolic tan activation # + def tanh(self, x): + ''' This is tan hyperbolic activation function. ''' + return (2./(1+np.exp(-2*x))) - 1 + + def tanh_diff(self, output): + ''' This is derivative of tan hyperbolic activation function. ''' + return 1 - (output)**2 + +''' +########### Experimental ########### +''' + + +class RNN: + ''' This is the class for Recursive neural nets ''' + pass + + +class CNN: + ''' This is the class for Convolutional neural nets ''' + pass diff --git a/libraries/LICENSE b/packages/LICENSE similarity index 100% rename from libraries/LICENSE rename to packages/LICENSE diff --git a/libraries/MANIFEST.in b/packages/MANIFEST.in similarity index 100% rename from libraries/MANIFEST.in rename to packages/MANIFEST.in diff --git a/libraries/README.rst b/packages/README.rst similarity index 100% rename from libraries/README.rst rename to packages/README.rst diff --git a/libraries/TODO b/packages/TODO similarity index 100% rename from libraries/TODO rename to packages/TODO diff --git a/libraries/dev/test_script_XOR-dev.ipynb b/packages/dev/test_script_XOR-dev.ipynb similarity index 100% rename from libraries/dev/test_script_XOR-dev.ipynb rename to packages/dev/test_script_XOR-dev.ipynb diff --git a/libraries/examples/notebooks/test_script_XOR.ipynb b/packages/examples/notebooks/test_script_XOR.ipynb similarity index 100% rename from libraries/examples/notebooks/test_script_XOR.ipynb rename to packages/examples/notebooks/test_script_XOR.ipynb diff --git a/libraries/examples/notebooks/test_script_iris.ipynb b/packages/examples/notebooks/test_script_iris.ipynb similarity index 100% rename from libraries/examples/notebooks/test_script_iris.ipynb rename to packages/examples/notebooks/test_script_iris.ipynb diff --git a/libraries/examples/test_data.py b/packages/examples/test_data.py similarity index 100% rename from libraries/examples/test_data.py rename to packages/examples/test_data.py diff --git a/libraries/examples/test_script.py b/packages/examples/test_script.py similarity index 100% rename from libraries/examples/test_script.py rename to packages/examples/test_script.py diff --git a/libraries/setup.py b/packages/setup.py similarity index 100% rename from libraries/setup.py rename to packages/setup.py