Regression Model Tensor flow 2.4 with Azure Machine learning

Use Case

• Check the version of tensorflow 2.4 and run a sample code to validate.
• Create a regression model
• Deep learning regression on regular tabular dataset

Requirements

• Azure Account
• Azure machine learning account
• Create a compute instance
• minst data set

Code

import tensorflow as tf; 
print(tf.__version__)
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
from tensorflow.keras.layers import Input, Dense, Activation,Dropout
from tensorflow.keras.models import Model

• Read the dataset
• data set is available in the folder

petrol_cons = pd.read_csv(r'petrol_consumption.csv')
petrol_cons.head()

• Split the dataset
• X data for features
• y for labels

y = petrol_cons.iloc[:,4]
X = petrol_cons.iloc[:,:4]
X = petrol_cons.iloc[:, 0:4].values
y = petrol_cons.iloc[:, 4].values

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
input_layer = Input(shape=(X.shape[1],))
dense_layer_1 = Dense(100, activation='relu')(input_layer)
dense_layer_2 = Dense(50, activation='relu')(dense_layer_1)
dense_layer_3 = Dense(25, activation='relu')(dense_layer_2)
output = Dense(1)(dense_layer_3)

model = Model(inputs=input_layer, outputs=output)
model.compile(loss="mean_squared_error" , optimizer="adam", metrics=["mean_squared_error"])
print(model.summary())
history = model.fit(X_train, y_train, batch_size=2, epochs=100, verbose=1, validation_split=0.2)
from sklearn.metrics import mean_squared_error
from math import sqrt

pred_train = model.predict(X_train)
print(np.sqrt(mean_squared_error(y_train,pred_train)))

pred = model.predict(X_test)
print(np.sqrt(mean_squared_error(y_test,pred)))

Originally published at https://github.com.