Royal Crown Tutorials

Getting familiar with Machine Learning | Neural networks vocabulary Neural networks always performs well with training. To perform training on the neural network we need data. Following is the example data set of iris flowers. It has 5 columns. Sepel length, Sepel width , Petal Length , Petal Width decides the species of the  iris flowers. We are going to predict the species of a particular flower depending on these characteristics. According to that knowledge we have 4 characteristics of data which we know.   We call them features.     So  Sepel length, Sepel width , Petal Length , Petal Width are the features of this data set. Species field is the label which we are going to predict. Rows of this data set are called examples . Which means , example is a set of features and labels for a particular record. We can optimize the neural network model by training. When we train the model by using the examples with labels we call it supervised machine learn

Training of a Keras model Numpy arrays of   input data and labels are used to train the Keras models. For training Fit function is used. For a single-input model with 2 classes (binary classification): model = Sequential() model.add(Dense(32, activation='relu', input_dim=100)) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop',               loss='binary_crossentropy',               metrics=['accuracy']) # Generate dummy data import numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(2, size=(1000, 1)) # Train the model, iterating on the data in batches of 32 samples model.fit(data, labels, epochs=10, batch_size=32) For a single-input model with 10 classes (categorical classification): model = Sequential() model.add(Dense(32, activation='relu', input_dim=100)) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop',    

Compilation of the module Compilation configure the learning process of the module. It is done via compile method. It takes 3 arguments.     Optimizer     Can use string identifier of an existing optimizer (rmsprop | adagrad) or instance of an existing Optimizer.     Loss Function Model always tries to minimize the loss. Can use existing string identifier of an loss function ( mse | categorical_crossentropy)or an objective function.     A list of metrics For any classification problem set metrics to accuracy. metrics=['accuracy'] metric could be a string identifier , existing metric or custom metric function. # For a binary classification problem model.compile(optimizer='rmsprop',               loss='binary_crossentropy',               metrics=['accuracy']) # For a mean squared error regression problem model.compile(optimizer='rmsprop',               loss='mse') # For custom metrics impo

Keras Sequential Model Creating the model | Specifying input size and batch size | specifying the activation function Sequential Model : Linear stack of layers. Create a Sequential Model by passing the layer instances to the constructor. Import sequential model from Keras models. from keras.models import Sequential Import dense layer and activation function from keras layers  from keras.layers import Dense, Activation Create sequential model  Input = can be of any dimension or a single element Shape = length along each dimension of the input Model needs to know the input shape it expects. Only the first layer of the sequential model should know its input shape. Batch = collection of inputs Batch Size = no of inputs Create the model           model = Sequential() Create the layer with batch size of 32 and input size of 784           Dense(32,input_dim=784) Add the layer to the model          model.add(Dense(32,input_dim=

Installing Tensor Flow Start a terminal Type the following command. pip3 install --upgrade tensorflow Keras development was primariliy backed by google. Keras API comes packaged in Tensor Flow as tf.Keras Installing Keras Type following command in the terminal pip3 install keras

Installing Python in Windows Click on the installer. Run the installer. Select add python 3.6 to path & Click install now. Set up complete.

Installing Keras Before installing keras you need to install one of its back end engines. Here we use Tensor Flow as the back end engine. There are other back end engines as well. This is the recommended one. Installing Tensor Flow On Windows Installing with native pip One of the following versions of python should be installed in your machine. Python 3.5.x 64-bit from python.org Python 3.6.x 64-bit from python.org Download this installer for windows (64 bit) machines Windows x86-64 executable installer

Getting Started With Machine Learning | Neural Networks Keras : Python Deep Learning Library Keras is a high level neural networks API. Written in python. Capable of running on top of tensor flow. This was developed with the focus on enabling fast experimentation. Advantages of using Keras Provides easy and fast prototyping User friendly  Modularity  Extensibility  Supports convolutional networks , recurrent networks and the combinations of both. Compatible with python 2.7 to 3.6