Neural networks using python

How To Create a Neural Network In Python – With And Without Keras

No matter which method you choose, working with a neural network to make a prediction is essentially the same:

1. Import the libraries . For example: import numpy as np
2. Define/create input data . For example, use numpy to create a dataset and an array of data values.
3. Add weights and bias (if applicable) to input features. These are learnable parameters, meaning that they can be adjusted during training.
   • Weights = input parameters that influences output
   • Bias = an extra threshold value added to the output
4. Train the network against known, good data in order to find the correct values for the weights and biases.
5. Test the Network against a set of test data to see how it performs.
6. Fit the model with hyperparameters (parameters whose values are used to control the learning process), calculate accuracy, and make a prediction.

Create a Neural Network from Scratch

In this example, I’ll use Python code and the numpy and scipy libraries to create a simple neural network with two nodes.

# Import python libraries required in this example: import numpy as np from scipy.special import expit as activation_function from scipy.stats import truncnorm # DEFINE THE NETWORK # Generate random numbers within a truncated (bounded) # normal distribution: def truncated_normal(mean=0, sd=1, low=0, upp=10): return truncnorm( (low - mean) / sd, (upp - mean) / sd, loc=mean, scale=sd) # Create the ‘Nnetwork’ class and define its arguments: # Set the number of neurons/nodes for each layer # and initialize the weight matrices: class Nnetwork: def __init__(self, no_of_in_nodes, no_of_out_nodes, no_of_hidden_nodes, learning_rate): self.no_of_in_nodes = no_of_in_nodes self.no_of_out_nodes = no_of_out_nodes self.no_of_hidden_nodes = no_of_hidden_nodes self.learning_rate = learning_rate self.create_weight_matrices() def create_weight_matrices(self): """ A method to initialize the weight matrices of the neural network""" rad = 1 / np.sqrt(self.no_of_in_nodes) X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad) self.weights_in_hidden = X.rvs((self.no_of_hidden_nodes, self.no_of_in_nodes)) rad = 1 / np.sqrt(self.no_of_hidden_nodes) X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad) self.weights_hidden_out = X.rvs((self.no_of_out_nodes, self.no_of_hidden_nodes)) def train(self, input_vector, target_vector): pass # More work is needed to train the network def run(self, input_vector): """ running the network with an input vector 'input_vector'. 'input_vector' can be tuple, list or ndarray """ # Turn the input vector into a column vector: input_vector = np.array(input_vector, ndmin=2).T # activation_function() implements the expit function, # which is an implementation of the sigmoid function: input_hidden = activation_function(self.weights_in_hidden @ input_vector) output_vector = activation_function(self.weights_hidden_out @ input_hidden) return output_vector # RUN THE NETWORK AND GET A RESULT # Initialize an instance of the class: simple_network = Nnetwork(no_of_in_nodes=2, no_of_out_nodes=2, no_of_hidden_nodes=4, learning_rate=0.6) # Run simple_network for arrays, lists and tuples with shape (2): # and get a result: simple_network.run([(3, 4)])

Figure 1. Array defined by the random values of the weights:

Create a Neural Network Using Keras

It’s difficult to replicate exactly the Python code in the previous example using Keras, so we’ll create a similar 2-node network model instead.

# Import python libraries required in this example: from keras.models import Sequential from keras.layers import Dense, Activation import numpy as np # Use numpy arrays to store inputs (x) and outputs (y): x = np.array([[0,0], [0,1], [1,0], [1,1]]) y = np.array([[0], [1], [1], [0]]) # Define the network model and its arguments. # Set the number of neurons/nodes for each layer: model = Sequential() model.add(Dense(2, input_shape=(2,))) model.add(Activation('sigmoid')) model.add(Dense(1)) model.add(Activation('sigmoid')) # Compile the model and calculate its accuracy: model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy']) # Print a summary of the Keras model: model.summary()

Figure 2. Summary of the Keras model:

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