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Machine Learning in Python
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robowolf

In this tutorial we will go through the Hello World project for a Convolutional Neural Network (which is used for images). First let's start off by saying what a Neural Network is. Think of two 'visible' layers. The input might have let's say 3 nodes and the output 2. To narrow it down we have a big set of hidden layers. That's the basics. Today we will be working with the MNIST dataset. I would recommend doing this outside of repl just because it will process faster. If you want to stay on your browser try https://colab.research.google.com/notebooks/intro.ipynb#recent=true. First we need to load up the dataset. In most cases creating the dataset is a hassle but since we have one that's preloaded it should be easy.

import tensorflow as tf import numpy as np mnist = tf.keras.datasets.mnist (x_train,y_train),(x_test,y_test) = mnist

What we've done here is imported our dataset and split it into variables.

x_train = tf.keras.utils.normalize(x_train) x_test = tf.keras.utils.normalize(x_test)

This will just normalize our data so we can better run it through. Now time for the fun part. Let's build our model.

model = tf.keras.models.Sequential() model.add(tf.keras.layers.Flatten()) #1d Databyte model.add(tf.keras.layers.Dense(128,activation = 'relu')) model.add(tf.keras.layers.Dense(128,activation='relu')) model.add(tf.keras.layers.Dense(10,activation ='softmax')

Now this might be a LOT to take in. What we're doing is adding hidden layers. These layers will be what our input goes through.
Now time to compile.

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])

You don't need to understand any of this right now. Just keep in mind that this is how we finish our model
TRAINING TIME

model.fit(x_train,y_train,epochs=3) #an epoch is how many times we run through our dataset.

To save and load do.

model.save('lol.model') model = tf.keras.models.load_model('lol.model')

Finally to predict just do

prediction =model.predict(x_test[0]) prediction_real = np.argmax(prediction[0])

Done. You've just created your first neural network. Hope you enjoyed - Robowolf

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