mirror of
https://github.com/dragen1860/TensorFlow-2.x-Tutorials.git
synced 2021-05-12 18:32:23 +03:00
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
import tensorflow as tf
|
|
import numpy as np
|
|
from tensorflow import keras
|
|
import os
|
|
|
|
|
|
class Regressor(keras.layers.Layer):
|
|
|
|
def __init__(self):
|
|
super(Regressor, self).__init__()
|
|
|
|
# here must specify shape instead of tensor !
|
|
# name here is meanless !
|
|
# [dim_in, dim_out]
|
|
self.w = self.add_variable('meanless-name', [13, 1])
|
|
# [dim_out]
|
|
self.b = self.add_variable('meanless-name', [1])
|
|
|
|
print(self.w.shape, self.b.shape)
|
|
print(type(self.w), tf.is_tensor(self.w), self.w.name)
|
|
print(type(self.b), tf.is_tensor(self.b), self.b.name)
|
|
|
|
|
|
def call(self, x):
|
|
|
|
x = tf.matmul(x, self.w) + self.b
|
|
|
|
return x
|
|
|
|
def main():
|
|
|
|
tf.random.set_seed(22)
|
|
np.random.seed(22)
|
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
|
assert tf.__version__.startswith('2.')
|
|
|
|
|
|
(x_train, y_train), (x_val, y_val) = keras.datasets.boston_housing.load_data()
|
|
#
|
|
x_train, x_val = x_train.astype(np.float32), x_val.astype(np.float32)
|
|
# (404, 13) (404,) (102, 13) (102,)
|
|
print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)
|
|
# Here has two mis-leading issues:
|
|
# 1. (x_train, y_train) cant be written as [x_train, y_train]
|
|
# 2.
|
|
db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(64)
|
|
db_val = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(102)
|
|
|
|
|
|
model = Regressor()
|
|
criteon = keras.losses.MeanSquaredError()
|
|
optimizer = keras.optimizers.Adam(learning_rate=1e-2)
|
|
|
|
for epoch in range(200):
|
|
|
|
for step, (x, y) in enumerate(db_train):
|
|
|
|
with tf.GradientTape() as tape:
|
|
# [b, 1]
|
|
logits = model(x)
|
|
# [b]
|
|
logits = tf.squeeze(logits, axis=1)
|
|
# [b] vs [b]
|
|
loss = criteon(y, logits)
|
|
|
|
grads = tape.gradient(loss, model.trainable_variables)
|
|
optimizer.apply_gradients(zip(grads, model.trainable_variables))
|
|
|
|
print(epoch, 'loss:', loss.numpy())
|
|
|
|
|
|
if epoch % 10 == 0:
|
|
|
|
for x, y in db_val:
|
|
# [b, 1]
|
|
logits = model(x)
|
|
# [b]
|
|
logits = tf.squeeze(logits, axis=1)
|
|
# [b] vs [b]
|
|
loss = criteon(y, logits)
|
|
|
|
print(epoch, 'val loss:', loss.numpy())
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main() |