Celsius to Fahrenheit conversion with Tesorflow

Celsius_to_Fahrenheit_conversion_with_tensorflow.py

# F = C * 1.8 + 32

print('The program starts')

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

import numpy as np
import matplotlib.pyplot as plt

#Store farenhite and their curresponding celcius into an array

celcius_q = np.array([], dtype=float)
farenhite_a = np.array([], dtype=float)

for c in range(5,100,5):
    f = c * 1.8 + 32
    celcius_q = np.append(celcius_q, c)
    farenhite_a = np.append(farenhite_a, f)

for i,e in enumerate(celcius_q):
    print('Celcius {} is equal to {}'.format(celcius_q[i], farenhite_a[i]))

#Creating the model
l0 = tf.keras.layers.Dense(units=1, input_shape=[1])

model = tf.keras.Sequential([l0])

#Compiling model
model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam(0.1))

#Training the model
history = model.fit(celcius_q, farenhite_a, epochs=500, verbose=False)
print('Finished training')

print('These are the layer weights {}'.format(l0.get_weights()))

print(model)

#Testing the model
while True:
     x = input('Enter in celsius: ')
     val = float(x)
     print(model.predict([val]))

# plt.xlabel('Epoc number')
# plt.ylabel('Loss magnitude')
# plt.plot(history.history['loss'])
# plt.show(block=True)