在 TensorFlow 中向模型外部添加变量
Externally add a Variable to a model in TensorFlow
我有一个现有的 TensorFlow 模型，我想在模型的参数列表中添加一个新的“参数”（一个 tf.Variable）（以便它可以训练）并将其从外部添加到模型的列表中参数/计算图。
我尝试过的一种方法是将新参数附加到模型的可训练权重列表中，就像这样（这里 new_parameter 是 tf.Variable）-
model.layers[-1].trainable_weights.extend([new_parameter])
model.compile(....)
但我不确定这是否是解决问题的最佳方式。在 PyTorch 中，我们有 nn.Parameter 而不是 tf.Variable，并且我们有 register_parameter 使用它我们可以将张量作为新参数注册到模型的参数列表中。 TensorFlow 中是否有 register_parameter 的等价物？或其他实现相同目标的方法？
自定义密集层也是可以的‼️
[样本]:
import os
from os.path import exists
import tensorflow as tf
import tensorflow_io as tfio
import matplotlib.pyplot as plt
import numpy as np
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = os.path.join('F:\datasets\downloads\Actors\train\Pikaploy', '*.tif')
PATH_2 = os.path.join('F:\datasets\downloads\Actors\train\Candidt Kibt', '*.tif')
files = tf.data.Dataset.list_files(PATH)
files_2 = tf.data.Dataset.list_files(PATH_2)
list_file = []
list_file_actual = []
list_label = []
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
for file in files.take(15):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(1)
for file in files_2.take(18):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(9)
checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\loggings.log"
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
log_dir = checkpoint_dir
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Function
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class MyDenseLayer(tf.keras.layers.Layer):
def __init__(self, num_outputs, num_add):
super(MyDenseLayer, self).__init__()
self.num_outputs = num_outputs
self.num_add = num_add
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_outputs])
def call(self, inputs):
temp = tf.add( inputs, self.num_add )
temp = tf.matmul(temp, self.kernel)
return temp
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
list_file = tf.cast( list_file, dtype=tf.int64 )
list_file = tf.constant( list_file, shape=(33, 1, 32, 32, 4), dtype=tf.int64)
list_label = tf.cast( list_label, dtype=tf.int64 )
list_label = tf.constant( list_label, shape=(33, 1, 1, 1), dtype=tf.int64)
dataset = tf.data.Dataset.from_tensor_slices(( list_file, list_label ))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
tf.keras.layers.Normalization(mean=3., variance=2.),
tf.keras.layers.Normalization(mean=4., variance=6.),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Reshape((128, 225)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
])
layer = MyDenseLayer(10, 5)
model.add(layer)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(192, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.summary()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Nadam'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50 )
model.save_weights(checkpoint_path)
plt.plot(history.history['loss'])
plt.show()
plt.close()
input("...")
[输出]:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
normalization (Normalizatio (None, 32, 32, 4) 0
n)
normalization_1 (Normalizat (None, 32, 32, 4) 0
ion)
conv2d (Conv2D) (None, 30, 30, 32) 1184
max_pooling2d (MaxPooling2D (None, 15, 15, 32) 0
)
dense (Dense) (None, 15, 15, 128) 4224
reshape (Reshape) (None, 128, 225) 0
bidirectional (Bidirectiona (None, 128, 192) 247296
l)
bidirectional_1 (Bidirectio (None, 192) 221952
nal)
my_dense_layer (MyDenseLaye (None, 10) 1920
r) *** custom layer added
flatten (Flatten) (None, 10) 0
dense_1 (Dense) (None, 192) 2112
dense_2 (Dense) (None, 10) 1930
=================================================================
Total params: 480,618
Trainable params: 480,618
Non-trainable params: 0
_________________________________________________________________
我有一个现有的 TensorFlow 模型，我想在模型的参数列表中添加一个新的“参数”（一个 tf.Variable）（以便它可以训练）并将其从外部添加到模型的列表中参数/计算图。
我尝试过的一种方法是将新参数附加到模型的可训练权重列表中，就像这样（这里 new_parameter 是 tf.Variable）-
model.layers[-1].trainable_weights.extend([new_parameter])
model.compile(....)
但我不确定这是否是解决问题的最佳方式。在 PyTorch 中，我们有 nn.Parameter 而不是 tf.Variable，并且我们有 register_parameter 使用它我们可以将张量作为新参数注册到模型的参数列表中。 TensorFlow 中是否有 register_parameter 的等价物？或其他实现相同目标的方法？
自定义密集层也是可以的‼️
[样本]:
import os
from os.path import exists
import tensorflow as tf
import tensorflow_io as tfio
import matplotlib.pyplot as plt
import numpy as np
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = os.path.join('F:\datasets\downloads\Actors\train\Pikaploy', '*.tif')
PATH_2 = os.path.join('F:\datasets\downloads\Actors\train\Candidt Kibt', '*.tif')
files = tf.data.Dataset.list_files(PATH)
files_2 = tf.data.Dataset.list_files(PATH_2)
list_file = []
list_file_actual = []
list_label = []
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
for file in files.take(15):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(1)
for file in files_2.take(18):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(9)
checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\loggings.log"
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
log_dir = checkpoint_dir
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Function
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class MyDenseLayer(tf.keras.layers.Layer):
def __init__(self, num_outputs, num_add):
super(MyDenseLayer, self).__init__()
self.num_outputs = num_outputs
self.num_add = num_add
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_outputs])
def call(self, inputs):
temp = tf.add( inputs, self.num_add )
temp = tf.matmul(temp, self.kernel)
return temp
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
list_file = tf.cast( list_file, dtype=tf.int64 )
list_file = tf.constant( list_file, shape=(33, 1, 32, 32, 4), dtype=tf.int64)
list_label = tf.cast( list_label, dtype=tf.int64 )
list_label = tf.constant( list_label, shape=(33, 1, 1, 1), dtype=tf.int64)
dataset = tf.data.Dataset.from_tensor_slices(( list_file, list_label ))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
tf.keras.layers.Normalization(mean=3., variance=2.),
tf.keras.layers.Normalization(mean=4., variance=6.),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Reshape((128, 225)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
])
layer = MyDenseLayer(10, 5)
model.add(layer)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(192, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.summary()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Nadam'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50 )
model.save_weights(checkpoint_path)
plt.plot(history.history['loss'])
plt.show()
plt.close()
input("...")
[输出]:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
normalization (Normalizatio (None, 32, 32, 4) 0
n)
normalization_1 (Normalizat (None, 32, 32, 4) 0
ion)
conv2d (Conv2D) (None, 30, 30, 32) 1184
max_pooling2d (MaxPooling2D (None, 15, 15, 32) 0
)
dense (Dense) (None, 15, 15, 128) 4224
reshape (Reshape) (None, 128, 225) 0
bidirectional (Bidirectiona (None, 128, 192) 247296
l)
bidirectional_1 (Bidirectio (None, 192) 221952
nal)
my_dense_layer (MyDenseLaye (None, 10) 1920
r) *** custom layer added
flatten (Flatten) (None, 10) 0
dense_1 (Dense) (None, 192) 2112
dense_2 (Dense) (None, 10) 1930
=================================================================
Total params: 480,618
Trainable params: 480,618
Non-trainable params: 0
_________________________________________________________________