AI News, Develop Your First Neural Network in Python With Keras Step-By-Step

Develop Your First Neural Network in Python With Keras Step-By-Step

Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.

It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.

The steps you are going to cover in this tutorial are as follows: This tutorial has a few requirements: If you need help with your environment, see the tutorial: Create a new file called keras_first_network.py and type or copy-and-paste the code into the file as you go.

This makes it easy to use directly with neural networks that expect numerical input and output values, and ideal for our first neural network in Keras.

We can specify the number of neurons in the layer as the first argument, the initialization method as the second argument as init and specify the activation function using the activation argument.

In this case, we initialize the network weights to a small random number generated from a uniform distribution (‘uniform‘), in this case between 0 and 0.05 because that is the default uniform weight initialization in Keras.

We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.

The second hidden layer has 8 neurons and finally, the output layer has 1 neuron to predict the class (onset of diabetes or not).

We must specify the loss function to use to evaluate a set of weights, the optimizer used to search through different weights for the network and any optional metrics we would like to collect and report during training.

The training process will run for a fixed number of iterations through the dataset called epochs, that we must specify using the nepochs argument.

We can also set the number of instances that are evaluated before a weight update in the network is performed, called the batch size and set using the batch_size argument.

No GPU is required for this example, but if you’re intersted in how to run large models on GPU hardware cheaply in the cloud, see this post: We have trained our neural network on the entire dataset and we can evaluate the performance of the network on the same dataset.

You can evaluate your model on your training dataset using the evaluate() function on your model and pass it the same input and output used to train the model.

This will generate a prediction for each input and output pair and collect scores, including the average loss and any metrics you have configured, such as accuracy.

Running this example, you should see a message for each of the 150 epochs printing the loss and accuracy for each, followed by the final evaluation of the trained model on the training dataset.

If you have more questions about making predictions with trained models, see this post: In this post, you discovered how to create your first neural network model using the powerful Keras Python library for deep learning.

Specifically, you learned the five key steps in using Keras to create a neural network or deep learning model, step-by-step including: Do you have any questions about Keras or about this tutorial? Ask

Getting started with the Keras Sequential model

You can create a Sequential model by passing a list of layer instances to the constructor: You can also simply add layers via the .add() method: The model needs to know what input shape it should expect.

For this reason, the first layer in a Sequential model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape.

There are several possible ways to do this: As such, the following snippets are strictly equivalent: Before training a model, you need to configure the learning process, which is done via the compile method.

Build your First Deep Learning Neural Network Model using Keras in Python

I have chosen my today’s topic as Neural Network because it is most the fascinating learning model in the world of data science and starters in Data Science think that Neural Network is difficult and its understanding requires knowledge of neurons, perceptron and blaahhhh…There is nothing like that, I have been working on Neural Network for quite a few month now and realized that it is so easy.

The fact of the matter is Keras is built on top of Tensorflow and Theano so this 2 insane library will be running in back-end whenever you run the program in Keras.

Deep understanding of NN(you can skip this if you don’t want to learn in depth) Now you can see that Country names are replaced by 0,1 and 2 while male and female are replaced by 0 and 1.

Dummy variable is difficult concept if you read in depth but don’t take tension, I have found this simple resource which will help you in understanding.

In Machine Learning, we always divide our data into training and testing part meaning that we train our model on training data and then we check the accuracy of a model on testing data.

Here we are using rectifier(relu) function in our hidden layer and Sigmoid function in our output layer as we want binary result from output layer but if the number of categories in output layer is more than 2 then use SoftMax function.

First argument is Optimizer, this is nothing but the algorithm you wanna use to find optimal set of weights(Note that in step 9 we just initialized weights now we are applying some sort of algorithm which will optimize weights in turn making out neural network more powerful.

Since out dependent variable is binary, we will have to use logarithmic loss function called ‘binary_crossentropy’, if our dependent variable has more than 2 categories in output then use ‘categorical_crossentropy’.

Develop Your First Neural Network in Python With Keras Step-By-Step

Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.

It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.

The steps you are going to cover in this tutorial are as follows: This tutorial has a few requirements: If you need help with your environment, see the tutorial: Create a new file called keras_first_network.py and type or copy-and-paste the code into the file as you go.

This makes it easy to use directly with neural networks that expect numerical input and output values, and ideal for our first neural network in Keras.

We can specify the number of neurons in the layer as the first argument, the initialization method as the second argument as init and specify the activation function using the activation argument.

In this case, we initialize the network weights to a small random number generated from a uniform distribution (‘uniform‘), in this case between 0 and 0.05 because that is the default uniform weight initialization in Keras.

We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.

The second hidden layer has 8 neurons and finally, the output layer has 1 neuron to predict the class (onset of diabetes or not).

We must specify the loss function to use to evaluate a set of weights, the optimizer used to search through different weights for the network and any optional metrics we would like to collect and report during training.

The training process will run for a fixed number of iterations through the dataset called epochs, that we must specify using the nepochs argument.

We can also set the number of instances that are evaluated before a weight update in the network is performed, called the batch size and set using the batch_size argument.

No GPU is required for this example, but if you’re intersted in how to run large models on GPU hardware cheaply in the cloud, see this post: We have trained our neural network on the entire dataset and we can evaluate the performance of the network on the same dataset.

You can evaluate your model on your training dataset using the evaluate() function on your model and pass it the same input and output used to train the model.

This will generate a prediction for each input and output pair and collect scores, including the average loss and any metrics you have configured, such as accuracy.

Running this example, you should see a message for each of the 150 epochs printing the loss and accuracy for each, followed by the final evaluation of the trained model on the training dataset.

If you have more questions about making predictions with trained models, see this post: In this post, you discovered how to create your first neural network model using the powerful Keras Python library for deep learning.

Specifically, you learned the five key steps in using Keras to create a neural network or deep learning model, step-by-step including: Do you have any questions about Keras or about this tutorial? Ask

Building A Deep Learning Model using Keras

Deep learning is an increasingly popular subset of machine learning.

A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training.

We will build a regression model to predict an employee’s wage per hour, and we will build a classification model to predict whether or not a patient has diabetes.

Note: The datasets we will be using are relatively clean, so we will not perform any data preprocessing in order to get our data ready for modeling.

Datasets that you will use in future projects may not be so clean — for example, they may have missing values — so you may need to use data preprocessing techniques to alter your datasets to get more accurate results.

I will not go into detail on Pandas, but it is a library you should become familiar with if you’re looking to dive further into data science and machine learning.

The ‘head()’ function will show the first 5 rows of the dataframe so you can check that the data has been read in properly and can take an initial look at how the data is structured.

For example, if you are predicting diabetes in patients, going from age 10 to 11 is different than going from age 60–61.

A smaller learning rate may lead to more accurate weights (up to a certain point), but the time it takes to compute the weights will be longer.

To train, we will use the ‘fit()’ function on our model with the following five parameters: training data (train_X), target data (train_y), validation split, the number of epochs and callbacks.

During training, we will be able to see the validation loss, which give the mean squared error of our model on the validation set.

We will set the validation split at 0.2, which means that 20% of the training data we provide in the model will be set aside for testing model performance.

Early stopping will stop the model from training before the number of epochs is reached if the model stops improving.

Sometimes, the validation loss can stop improving then improve in the next epoch, but after 3 epochs in which the validation loss doesn’t improve, it usually won’t improve again.

We can see that by increasing our model capacity, we have improved our validation loss from 32.63 in our old model to 28.06 in our new model.

How to Reshape Input Data for Long Short-Term Memory Networks in Keras

I got an exception “ValueError: Input arrays should have the same number of samples as target arrays.

Found 1 input samples and 21 target samples”.

[-0.31399291 0.6081381 ]] =>print y_train 0

out, dtype: int64 =>print(y_train.shape) (21,) =>print X_train.shape (21, 2) =>print X_test.shape (8, 2) I

model.compile(optimizer=’rmsprop’,loss=’categorical_crossentropy’,metrics=[‘categorical_accuracy’]) history = model.fit(X_train,y_train,batch_size =13, epochs = 14) —————————————————————————ValueError Traceback (most recent call last)

1 history = model.fit(X_train,y_train,batch_size =13, epochs = 14) /home/siji/anaconda2/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)

895 def evaluate(self, x, y, batch_size=32, verbose=1, /home/siji/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)

1557 do_validation = False /home/siji/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)

1423 self._feed_loss_fns, /home/siji/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc in _check_array_lengths(inputs, targets, weights)

249 ‘the same number of samples as target arrays.

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