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Simplest neural network with TensorFlow

This page presents the simplest single layer neural network you can create with TensorFlow. This example shows and details how to create your first simple neural networks.

The following has been performed with the following version:

Try the example online on Google Colaboratory.

Problem definition

The goal of this simple example is to approximate a linear function given by the following equation:

$$ y = a.x + b = 0.6x + 2 $$

The blue dots are the training set, the red line is the output of the network:

Result of the single layer neural network

Source code

Here is the complete source code. Each line is explained in the next section. This example can be run online on Google Colaboratory

import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras

# Parameters (y = a*x + b)
a=0.6
b=2

# Create noisy data
x_data = np.linspace(-10, 10, num=100000)
y_data = a * x_data + b + np.random.normal(size=100000)

# Create the model 
model = keras.Sequential()
model.add(keras.layers.Dense(units = 1, activation = 'linear', input_shape=[1]))
model.compile(loss='mse', optimizer="adam")

# Display the model (only 2 parameters to optimize)
model.summary()

# Learn
model.fit( x_data, y_data, epochs=5, verbose=1 )

# Predict (compute) the output 
y_predicted = model.predict(x_data)

# Display the result
plt.scatter(x_data[::500], y_data[::500])
plt.plot(x_data, y_predicted, 'r', linewidth=4)
plt.grid()
plt.show()

Explanation

First, we import the libraries:

import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras

Then, we create the training data. x_data is composed of 100000 points, and normal noise is added to the y-coordinate of each point:

# Parameters (y = a*x + b)
a=0.6
b=2

# Create noisy data
x_data = np.linspace(-10, 10, num=100000)
y_data = a * x_data + b + np.random.normal(size=100000)

Here is a sample of the training set:

Sample of training dataset for our simple neural network

Once our training dataset is built, we can create our network. In TensorFlow this is called the model:

# Create the model 
model = keras.Sequential()
model.add(keras.layers.Dense(units = 1, activation = 'linear', input_shape=[1]))
model.compile(loss='mse', optimizer="adam")

# Display the model (only 2 parameters to optimize)
model.summary()

Let's analyse the code. First we create a sequential Keras model. The Sequential model is a linear stack of layers. Keras is the core library for building neural networks. We add a regular densely-connected layer to our model (Dense) with:

The model is compiled with the following optimization parameters:

Once the model is defined, let's train our network:

# Learn
model.fit( x_data, y_data, epochs=5, verbose=1)

It should display something like:

Train on 100000 samples
Epoch 1/5
100000/100000 [==============================] - 4s 40us/sample - loss: 34.6061
Epoch 2/5
100000/100000 [==============================] - 4s 35us/sample - loss: 1.0512
Epoch 3/5
100000/100000 [==============================] - 3s 26us/sample - loss: 1.0080
Epoch 4/5
100000/100000 [==============================] - 3s 29us/sample - loss: 1.0080
Epoch 5/5
100000/100000 [==============================] - 3s 28us/sample - loss: 1.0082

Once trainning is over, we can predict and display the output for each input:

# Predict (compute) the output 
y_predicted = model.predict(x_data)

# Display the result
plt.scatter(x_data[::500], y_data[::500])
plt.plot(x_data, y_predicted, 'r', linewidth=4)
plt.grid()
plt.show()

Here is the result:

Linear regression with TensorFlow

Let's have a deeper look at our network. Once the network is trained, let's print the weights of our network:

>>> print( model.trainable_variables )
[<tf.Variable 'dense_6/kernel:0' shape=(1, 1) dtype=float32, numpy=array([[0.5970049]], dtype=float32)>, <tf.Variable 'dense_6/bias:0' shape=(1,) dtype=float32, numpy=array([1.9903255], dtype=float32)>]

Our weights are 0.5970049 and 1.9903255, almost 0.6 and 2 our initial parameters!

Download

You can try this example online on Google Colaboratory

See also


Last update : 03/14/2020