Creating the model
#Set the random seed
tf.random.set_seed(42)
#Create a neural classification
classi_model1=tf.keras.Sequential([
tf.keras.layers.Dense(100,activation='relu'),
tf.keras.layers.Dense(1,activation='softmax')
])
#Compile the model
classi_model1.compile(loss=tf.keras.losses.BinaryCrossentropy(),
optimizer = tf.keras.optimizers.Adam(),
metrics=["accuracy"])
#Fit the model
classi_model1.fit(X,y,epochs=50,verbose=0)
#Evaluate the model
classi_model1.evaluate(X,y)
> Using same model for regression problem
#Let's see if our model is working for regression problem
tf.random.set_seed(42)
#Create some regression data
X_regression = tf.range(0,1000,5)
y_regression = tf.range(100,1100,5) #y=X+100
#Split our regression data into training data
X_train = X_regression[:150]
X_test = X_regression[150:]
y_train = y_regression[:150]
y_test = y_regression[150:]
X_train = tf.convert_to_tensor(X_train)
y_train = tf.convert_to_tensor(y_train)
#Fit the regression data to classification model
classi_model1.fit(X_train, y_train, epochs=100)
Error image
[1]: https://i.stack.imgur.com/2io31.png