In this tutorial, we'll learn how to fit and predict regression data with the CNN 1D model with Keras in Python. The tutorial covers:

- Preparing the data
- Defining and fitting the model
- Predicting and visualizing the results
- Source code listing

from sklearn.datasets import load_boston from keras.models import Sequential from keras.layers import Dense, Conv1D, Flatten from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import matplotlib.pyplot as plt

**Preparing the data**

We can use the Boston housing dataset as target regression data. First, we'll load the dataset and check the data dimensions of both x and y.

boston = load_boston() x, y = boston.data, boston.target

print(x.shape)

(506, 13)

An x data has two dimensions that are the number of rows and columns. Here, we need to add the third dimension that will be the number of the single input row. In our example, it becomes 1 that is [13, 1]. We'll reshape the x data accordingly.

x = x.reshape(x.shape[0], x.shape[1], 1) print(x.shape)

(506, 13, 1)

Next, we'll split the data into the train and test parts.

xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15)

**Defining and fitting the model**

We'll define the Keras sequential model and add a one-dimensional convolutional layer. Input shape becomes as it is defined above (13,1). We'll add Flatten and Dense layers and compile it with optimizers.

model = Sequential() model.add(Conv1D(32, 2, activation="relu", input_shape=(13, 1))) model.add(Flatten()) model.add(Dense(64, activation="relu")) model.add(Dense(1)) model.compile(loss="mse", optimizer="adam")

`model.summary()`

_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv1d_10 (Conv1D) (None, 12, 32) 96 _________________________________________________________________ flatten_8 (Flatten) (None, 384) 0 _________________________________________________________________ dense_355 (Dense) (None, 64) 24640 _________________________________________________________________ dense_356 (Dense) (None, 1) 65 ================================================================= Total params: 24,801 Trainable params: 24,801 Non-trainable params: 0 _________________________________________________________________

Next, we'll fit the model with train data.

model.fit(xtrain, ytrain, batch_size=12,epochs=200, verbose=0)

**Predicting and visualizing the results**

Now we can predict the test data with the trained model.

ypred = model.predict(xtest)

We can evaluate the model, check the mean squared error rate (MSE) of the predicted result, and visualize the result in a plot.

print(model.evaluate(xtrain, ytrain))

21.21026409947595

print("MSE: %.4f" % mean_squared_error(ytest, ypred))

MSE: 19.8953

x_ax = range(len(ypred)) plt.scatter(x_ax, ytest, s=5, color="blue", label="original") plt.plot(x_ax, ypred, lw=0.8, color="red", label="predicted") plt.legend() plt.show()

In this tutorial, we've briefly learned how to fit and predict regression data with the keras CNN model in Python. The full source code is listed below.

**Source code listing**

from sklearn.datasets import load_boston from keras.models import Sequential from keras.layers import Dense, Conv1D, Flatten from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import matplotlib.pyplot as plt boston = load_boston() x, y = boston.data, boston.target print(x.shape) x = x.reshape(x.shape[0], x.shape[1], 1) print(x.shape) xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15) model = Sequential() model.add(Conv1D(32, 2, activation="relu", input_shape=(13,1))) model.add(Flatten()) model.add(Dense(64, activation="relu")) model.add(Dense(1)) model.compile(loss="mse", optimizer="adam") model.summary() model.fit(xtrain, ytrain, batch_size=12,epochs=200, verbose=0) ypred = model.predict(xtest) print(model.evaluate(xtrain, ytrain)) print("MSE: %.4f" % mean_squared_error(ytest, ypred)) x_ax = range(len(ypred)) plt.scatter(x_ax, ytest, s=5, color="blue", label="original") plt.plot(x_ax, ypred, lw=0.8, color="red", label="predicted") plt.legend() plt.show()

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