# AI News, Train A One Layer Feed Forward Neural Network in TensorFlow With ReLU Activation

- On Wednesday, June 6, 2018
- By Read More

## Train A One Layer Feed Forward Neural Network in TensorFlow With ReLU Activation

To train our model, we need to tell the model what the correct answer is and we're going to do that by feeding in the correct answers.

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data

However, you'll notice here that instead of having a 784-dimensional vector, we have a 10-dimensional vector.

y_ which represents the correct values that we are trying to get our neural network to learn is a 10-dimensional vector as each vector corresponds to the true probability for each of the different classes, namely 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.

Once we have defined our predictions and then the true labels, we're going to use cross entropy to compare them and to produce a numerical value of how close our answer is to the correct answer.

Because we're feeding in the raw output of the ReLU, we're going to call it tf.nn.softmax_cross_entropy_with_logits.

So what this is saying is that using the TensorFlow GradientDescentOptimizer with a learning rate of 0.001, minimize the variable that we've defined as our cross entropy.

Our cross entropy here is defined as the cross entropy between logits y and the labels y_, again with the outputs of our model and the true values.

We're going to train it for 50 steps which we'll handle just using a standard Python for loop.

What this says is from the training set, pull a new batch of 100 samples from there.

Just to show that this runs, we're going to produce this and it should return without any errors.

So just to make sure that it's doing something, we'll tell it to print out what step it's on.

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_) train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)

To train our model, we need to tell the model what the correct answer is and we're going to do that by feeding in the correct answers.

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data

However, you'll notice here that instead of having a 784-dimensional vector, we have a 10-dimensional vector.

y_ which represents the correct values that we are trying to get our neural network to learn is a 10-dimensional vector as each vector corresponds to the true probability for each of the different classes, namely 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.

Once we have defined our predictions and then the true labels, we're going to use cross entropy to compare them and to produce a numerical value of how close our answer is to the correct answer.

Because we're feeding in the raw output of the ReLU, we're going to call it tf.nn.softmax_cross_entropy_with_logits.

So what this is saying is that using the TensorFlow GradientDescentOptimizer with a learning rate of 0.001, minimize the variable that we've defined as our cross entropy.

Our cross entropy here is defined as the cross entropy between logits y and the labels y_, again with the outputs of our model and the true values.

We're going to train it for 50 steps which we'll handle just using a standard Python for loop.

What this says is from the training set, pull a new batch of 100 samples from there.

Just to show that this runs, we're going to produce this and it should return without any errors.

So just to make sure that it's doing something, we'll tell it to print out what step it's on.

- On Monday, January 21, 2019

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