
import tensorflow as tf
import numpy as np

celcius_q = np.array([-40,-10,0,8,15,22,38],dtype=float)
far_q = np.array([-40,14,32,46,59,72,100],dtype=float)

layer1 = tf.keras.layers.Dense(units=1,input_shape=[1])

model = tf.keras.Sequential([layer1])

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

import matplotlib.pyplot as plt
plt.plot(history.history['loss'])

history = model.fit(celcius_q,far_q,epochs=500,verbose=False)

print(model.predict([100])) # sampe input for prediction 

513972771d44dbf90f4cd0f44e9b91add1fd7cb1
<mask token>

<mask token>
model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam
    (0.1))
<mask token>
plt.plot(history.history['loss'])
<mask token>
print(model.predict([100]))

<mask token>
celcius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
far_q = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)
layer1 = tf.keras.layers.Dense(units=1, input_shape=[1])
model = tf.keras.Sequential([layer1])
model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam
    (0.1))
<mask token>
plt.plot(history.history['loss'])
history = model.fit(celcius_q, far_q, epochs=500, verbose=False)
print(model.predict([100]))

import tensorflow as tf
import numpy as np
celcius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
far_q = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)
layer1 = tf.keras.layers.Dense(units=1, input_shape=[1])
model = tf.keras.Sequential([layer1])
model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam
    (0.1))
import matplotlib.pyplot as plt
plt.plot(history.history['loss'])
history = model.fit(celcius_q, far_q, epochs=500, verbose=False)
print(model.predict([100]))


import tensorflow as tf
import numpy as np

celcius_q = np.array([-40,-10,0,8,15,22,38],dtype=float)
far_q = np.array([-40,14,32,46,59,72,100],dtype=float)

layer1 = tf.keras.layers.Dense(units=1,input_shape=[1])

model = tf.keras.Sequential([layer1])

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

import matplotlib.pyplot as plt
plt.plot(history.history['loss'])

history = model.fit(celcius_q,far_q,epochs=500,verbose=False)

print(model.predict([100])) # sampe input for prediction 
