Utilize 500 as epochs.
Step 1 - Import the modules
import keras
from keras.datasets import boston_housing
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping
from sklearn import preprocessing
from sklearn.preprocessing import scale
Step 2 - Load Data
Allow us to import Boston housing dataset
(x_train, y_train>
, (x_test, y_test>
= boston_housing.load_data(>
Here,
boston_housing is a dataset given by Keras. It addresses an assortment of housing data in Boston area, each having 13 highlights.
Step 3 - Process the data
Allow us to change the dataset as indicated by our model, so that, we can take care of into our model. The information can be changed utilizing beneath code −
x_train_scaled = preprocessing.scale(x_train>
scaler = preprocessing.StandardScaler(>
.fit(x_train>
x_test_scaled = scaler.transform(x_test>
Here, we have standardized the preparation information utilizing sklearn.preprocessing.scale function. preprocessing.StandardScaler(>
.fit work returns a scalar with the standardized mean and standard deviation of the preparation information, which we can apply to the test information utilizing scalar.transform work. This will standardize the test information also with the very setting as that of preparing information.
Step 4 - create the model
Allow us to create a model
model = Sequential(>
model.add(Dense(64, kernel_initializer = 'normal', activation = 'relu',
input_shape = (13,>
>
>
model.add(Dense(64, activation = 'relu'>
>
model.add(Dense(1>
>
Step 5 - Compile the model
Allow us to assemble the model utilizing chosen misfortune work, analyzer and metrics.
model.compile(
loss = 'mse',
optimizer = RMSprop(>
,
metrics = ['mean_absolute_error']
>
Step 6 - Train the model
Allow us to train the model utilizing fit(>
method.
history = model.fit(
x_train_scaled, y_train,
batch_size=128,
epochs = 500,
verbose = 1,
validation_split = 0.2,
callbacks = [EarlyStopping(monitor = 'val_loss', patience = 20>
]
>
Here, we have utilized callback work, EarlyStopping. The reason for this callback is to screen the misfortune value during every epoch and contrast it and past epoch misfortune worth to track down the improvement in the preparation. In the event that there is no improvement for the persistence times, the entire process will be stopped.
Step 7 - Evaluate the model
Allow us to evaluate the model using test impormation
score = model.evaluate(x_test_scaled, y_test, verbose = 0>
print('Test loss:', score[0]>
print('Test accuracy:', score[1]>
Step 8 - Predict
prediction = model.predict(x_test_scaled>
print(prediction.flatten(>
>
print(y_test>
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