gistfile1.txt

import tensorflow as tf
iport numpy as np
from tensorflow import keras
# Create an 1*1 layer neuron 
model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])

# Setting optimizer and loss function 
model.compile(optimizer='sgd', loss='mean_squared_error')

# Training data 
xs = np.array([1, 2, 3, 4], dtype=int)
ys = np.array([100, 150, 200, 250], dtype=int)

# Training mode for 500 iteration 
model.fit(xs, ys, epochs=500)

# Predict the output
print(model.predict([7.0]))m