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| import tensorflow as tf
from tensorflow.keras import models,layers,optimizers,losses,metrics
@tf.function
def printbar():
ts = tf.timestamp()
today_ts = ts%(24*60*60)
hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
minite = tf.cast((today_ts%3600)//60,tf.int32)
second = tf.cast(tf.floor(today_ts%60),tf.int32)
def timeformat(m):
if tf.strings.length(tf.strings.format("{}",m))==1:
return(tf.strings.format("0{}",m))
else:
return(tf.strings.format("{}",m))
timestring = tf.strings.join([timeformat(hour),timeformat(minite),
timeformat(second)],separator = ":")
tf.print("=========="*8,end = "")
tf.print(timestring)
n = 800
X = tf.random.uniform([n,2],minval=-10,maxval=10)
w0 = tf.constant([[2.0],[-1.0]])
b0 = tf.constant(3.0)
Y = X@w0 + b0 + tf.random.normal([n,1],mean = 0.0,stddev= 2.0)
ds_train = tf.data.Dataset.from_tensor_slices((X[0:n*3//4,:],Y[0:n*3//4,:])) \
.shuffle(buffer_size = 1000).batch(20) \
.prefetch(tf.data.experimental.AUTOTUNE) \
.cache()
ds_valid = tf.data.Dataset.from_tensor_slices((X[n*3//4:,:],Y[n*3//4:,:])) \
.shuffle(buffer_size = 1000).batch(20) \
.prefetch(tf.data.experimental.AUTOTUNE) \
.cache()
tf.keras.backend.clear_session()
class MyModel(models.Model):
def __init__(self):
super(MyModel, self).__init__()
def build(self,input_shape):
self.dense1 = layers.Dense(1)
super(MyModel,self).build(input_shape)
def call(self, x):
y = self.dense1(x)
return(y)
model = MyModel()
model.build(input_shape =(None,2))
model.summary()
optimizer = optimizers.Adam()
loss_func = losses.MeanSquaredError()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_metric = tf.keras.metrics.MeanAbsoluteError(name='train_mae')
valid_loss = tf.keras.metrics.Mean(name='valid_loss')
valid_metric = tf.keras.metrics.MeanAbsoluteError(name='valid_mae')
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features)
loss = loss_func(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss.update_state(loss)
train_metric.update_state(labels, predictions)
@tf.function
def valid_step(model, features, labels):
predictions = model(features)
batch_loss = loss_func(labels, predictions)
valid_loss.update_state(batch_loss)
valid_metric.update_state(labels, predictions)
@tf.function
def train_model(model,ds_train,ds_valid,epochs):
for epoch in tf.range(1,epochs+1):
for features, labels in ds_train:
train_step(model,features,labels)
for features, labels in ds_valid:
valid_step(model,features,labels)
logs = 'Epoch={},Loss:{},MAE:{},Valid Loss:{},Valid MAE:{}'
if epoch%100 ==0:
printbar()
tf.print(tf.strings.format(logs,
(epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result())))
tf.print("w=",model.layers[0].kernel)
tf.print("b=",model.layers[0].bias)
tf.print("")
train_loss.reset_states()
valid_loss.reset_states()
train_metric.reset_states()
valid_metric.reset_states()
train_model(model,ds_train,ds_valid,400)
Model: "my_model"
_________________________________________________________________
Layer (type) Output Shape Param
=================================================================
dense (Dense) multiple 3
=================================================================
Total params: 3
Trainable params: 3
Non-trainable params: 0
_________________________________________________________________
================================================================================18:17:08
Epoch=100,Loss:67.9662247,MAE:6.36445856,Valid Loss:65.3885117,Valid MAE:6.17629
w= [[1.65662384]
[-1.01629746]]
b= [1.92026019]
================================================================================18:17:16
Epoch=200,Loss:36.144165,MAE:4.0302186,Valid Loss:35.4477425,Valid MAE:4.01481533
w= [[1.99435031]
[-1.00531375]]
b= [3.02523756]
================================================================================18:17:25
Epoch=300,Loss:25.3425236,MAE:3.20795441,Valid Loss:25.308445,Valid MAE:3.25203133
w= [[1.99592912]
[-1.00504756]]
b= [3.08958364]
================================================================================18:17:34
Epoch=400,Loss:19.9554043,MAE:2.79780984,Valid Loss:20.2524834,Valid MAE:2.87172508
w= [[1.99595356]
[-1.00504148]]
b= [3.08971953]
|
