## Keras and Tensorflow Basics in Python – Part II

Another simple example of utilizing Keras to predict a multi-classification problem. Details in the Jupyter notebook below.
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# Tag: deep-learning

## Keras and Tensorflow Basics in Python – Part II

## Keras and Tensorflow Pt III – Classification Example

## Choosing the right number of layers/neurons for a Neural Network (Python)

## Keras and Tensorflow Pt II – Regression Example

## Keras and Tensorflow Basics in Python – Simple Example

Another simple example of utilizing Keras to predict a multi-classification problem. Details in the Jupyter notebook below.
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This is a good simple example of a classification problem utilizing Keras and Tensorflow. In addition, I'm utilizing early stopping in an attempt to avoid overfitting in the model. You'll notice this take effect as the model stops training well before the 600 set epochs.
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This seems to be a very confusing subject for most, and I've had difficulty while learning how to setup Keras NN models as the addition/subtraction of layers and neurons creates vastly different outcomes in model. Normally I wouldn't just link out to others, but there is a very well written synopsis found on StackExchange below that lays it out in a very simple fashion. Very brief summary:
Input (first) layer: Neurons = Number of features in the datasetHidden layer(s): Neurons = Somewhere between 1 and the amount in the input later (take the mean); Number of hidden layers: 1 works for *most* applications, maybe none.Output (last) layer: exactly 1 unless it's a classification problem and you utilize the softmax activation, in which case the number equals the number of classes you are predicting
https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
Meaning in the case of a dataset with 20 features:
#Example Keras Binary Classification model
model = Sequential()
model.add(Dense(20, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam')
#Example Keras Multi-Class model
model = Sequential()
model.add(Dense(20, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(3, activation='softmax')) #If I...

This is a more complex example of Keras, utilizing Regression. This utilizes a good sized dataset from Kaggle, but does contain a little bit of data cleansing before we can build out the model. Unfortunately the model we end up building isn't perfect and requires more tuning or some final dataset alterations, but it's a good example none the less. More information below.
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Below is a small example showing how to utilize Keras/Tensorflow 2.0 to predict a value utilizing a small dataset. More explanations to follow in the Jupyter notebook below...
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