What are the metrics through which we can evaluate a regression model in keras?

What are the metrics through which we can evaluate a regression model in keras?

What are the metrics through which we can evaluate a regression model in keras?

This recipe explains what are the metrics through which we can evaluate a regression model in keras


Recipe Objective

The metrics through which we can evaluate a regression model in keras Keras Have provided us many ways to evaluate the Regression and Classification model when we train a deep Learning Model. But our focus is to evaluate a Regression Model

Certain types of Metrics can be used for regression Models: Mean Squared Error Mean Absolute Error Mean Absolute Percentage Error Cosine Proximity We will show you the implementation of all Metrics and visualize the Mean Squared Error and Mean Absolute Error.

Step 1- Importing Libraries

from numpy import array from keras.models import Sequential from keras.layers import Dense from matplotlib import pyplot

Step 2- Creating arrays

We will Define two Arrays Let's consider y_pred is our predicted array and y_actual is our actual array.

# prepare sequence y_pred = array([0.4, 0.6, 0.8, 1.0, 0.5, 0.4, 0.3, 0.2, 0.1, 0.8]) y_actual = array([-0.4, -0.3, 0.8, 0.9, 0, 1.2, 0.3, 2.4, 0.3, 0.3])

Step 3- We will add layers and other parameters to create our model.

We will define all the Metrics while compiling the model as you can see below.

# create model model = Sequential() model.add(Dense(2, input_dim=1)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['mse', 'mae','mape', 'cosine_proximity'])

Step 4- Training our model

We will train our model with the defined parameters

# train model history = model.fit(y_pred, y_actual, epochs=50, batch_size=len(y_pred), verbose=2)

Step 5- We will plot our models (MSE and MAE)

# plot metrics pyplot.plot(history.history['mse']) pyplot.plot(history.history['mae']) pyplot.show()

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