tensorflow homework

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
xx = np.arange (-5, 10, 0.5)
yy = 3*xx**2 - 2*xx + 1
print (xx)
print (yy)
print("версия Tensorflow:", tf.__version__)
print("используемое GPU:", tf.test.gpu_device_name())
xx = np.array(xx)
yy = np.array(yy, dtype=float)
for i, c in enumerate(xx):
  print("{} xx= {} yy "
  .format(c, yy[i]))
l0 = tf.keras.layers.Dense(units=1, input_shape=[1])
model = tf.keras.Sequential([l0])
model.compile(loss='mean_squared_logarithmic_error', optimizer=tf.keras.optimizers.Adam(0.1))
history = model.fit(xx, yy, epochs=3000, verbose=False)
print ("Завершили тренировку модели")
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.plot(history.history['loss'])
print(model.predict([-10.0]))
print("Это значения переменных слоя: {}".format(l0.get_weights()))


xx = np.linspace(-100, 100, 1000)
yy = np.sin(xx)

plt.plot(xx, yy)
plt.show()



from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

xx = np.linspace(-100, 100, 1000)
yy = np.sin(xx)

plt.plot(xx, yy)
plt.show()

print("версия Tensorflow:", tf.__version__)
print("используемое GPU:", tf.test.gpu_device_name())
for i, c in enumerate(xx):
  print("{} xx= {} yy "
  .format(c, yy[i]))
l0 = tf.keras.layers.Dense(units=1, input_shape=[1])
model = tf.keras.Sequential([l0])
model.compile(loss='mean_squared_logarithmic_error', optimizer=tf.keras.optimizers.Adam(0.1))
history = model.fit(xx, yy, epochs=3000, verbose=False)
print ("Завершили тренировку модели")
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.plot(history.history['loss'])
print(model.predict([-10.0]))
print("Это значения переменных слоя: {}".format(l0.get_weights()))



from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
xx = np.linspace(-2, 2, 100)
yy = np.exp(xx + 2)

plt.plot(xx, yy)
plt.show()
print("версия Tensorflow:", tf.__version__)
print("используемое GPU:", tf.test.gpu_device_name())
xx = np.array(xx)
yy = np.array(yy, dtype=float)
for i, c in enumerate(xx):
  print("{} xx= {} yy "
  .format(c, yy[i]))
l0 = tf.keras.layers.Dense(units=1, input_shape=[1])
model = tf.keras.Sequential([l0])
model.compile(loss='mean_squared_logarithmic_error', optimizer=tf.keras.optimizers.Adam(0.1))
history = model.fit(xx, yy, epochs=3000, verbose=False)
print ("Завершили тренировку модели")
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.plot(history.history['loss'])
print(model.predict([-10.0]))
print("Это значения переменных слоя: {}".format(l0.get_weights()))