Tensorflow 2.0 相同的模型结构和超参数导致在不同的调用方式下表现不同
Tensorflow 2.0 Identical model structure and hyper parameters result in different performance in different calling approaches
那里。我是初学者，正在学习 Tensorflow 2.0。我有一个模型以 3 种不同的方法调用。而且表现也不同。谁能告诉我为什么会这样？
模型构建调用方法：
import os, sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, models, layers, regularizers, optimizers
def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
def mninst_dataset():
(x_train, y_train), (x_eval, y_eval) = datasets.mnist.load_data()
print('x_train/y_train shape:', x_train.shape, y_train.shape)
y_train = tf.one_hot(y_train, depth=10)
y_eval = tf.one_hot(y_eval, depth=10)
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
ds_train = ds_train.map(prepare_mnist_features_and_labels)
ds_train = ds_train.shuffle(x_train.shape[0]).batch(128)
ds_eval = tf.data.Dataset.from_tensor_slices((x_eval, y_eval))
ds_eval = ds_eval.map(prepare_mnist_features_and_labels)
ds_eval = ds_eval.shuffle(x_eval.shape[0]).batch(128)
sample = next(iter(ds_train))
print('sample: ', sample[0].shape, sample[1].shape)
return ds_train, ds_eval
def main():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
trainset, evalset = mninst_dataset()
model = keras.Sequential()
model.add(layers.Reshape(target_shape=[28, 28, 1], input_shape=[28, 28]))
model.add(layers.Conv2D(filters=32, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME"))
model.add(layers.MaxPool2D(pool_size=(2,2), strides=[1,1], padding="SAME"))
model.add(layers.Conv2D(filters=64, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME"))
model.add(layers.MaxPool2D(pool_size=(2,2), strides=[2,2], padding="SAME"))
model.add(layers.Flatten())
model.add(layers.Dense(units=512, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01)))
model.add(layers.Dense(units=10, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01)))
model.compile(optimizer=optimizers.Adam(lr=0.01), loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
model.fit(trainset.repeat(), epochs=30, steps_per_epoch=500,
validation_data=evalset.repeat(), validation_steps=10)
if __name__=='__main__':
main()
构建模型的第二种方法运行如下：
import os, sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, models, layers, regularizers, optimizers
from tqdm import tqdm
def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
def mnist_dataset():
(x_train, y_train), (x_eval, y_eval) = datasets.mnist.load_data()
# y_train = tf.one_hot(y_train, depth=10)
# y_eval = tf.one_hot(y_eval, depth=10)
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
ds_train = ds_train.map(prepare_mnist_features_and_labels)
# Test: replace x_train.shape[0] by the number of the training samples, which is 60000
ds_train = ds_train.shuffle(x_train.shape[0]).batch(128)
ds_eval = tf.data.Dataset.from_tensor_slices((x_eval, y_eval))
ds_eval = ds_eval.map(prepare_mnist_features_and_labels)
ds_eval = ds_eval.shuffle(x_eval.shape[0]).batch(128)
# sample = next(iter(ds_train))
# print('sample: ', sample[0].shape, sample[1].shape)
return ds_train, ds_eval
# tf.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name=None):
# labels: Tensof of shape [d_0, d_1, ..., d_{r-1}]. Each label must be an index in [0, num_classes]
# logits: Unscaled of log probabilities of shape [d_0, d_1, ..., d_{r-1}, num_classes]
# A common use is to have logits of shape [batch_size, num_classes] and have labels of shape [batch_size]
def compute_loss(logits, labels):
# print(logits.numpy())
# print(labels.numpy())
return tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits
)
)
def compute_accuracy(logits, labels):
predictions = tf.argmax(logits, axis=1)
# print(predictions)
# print(labels)
# print(list(zip(predictions.numpy(), labels.numpy())))
return tf.reduce_mean(tf.cast(tf.equal(predictions, labels), tf.float32))
def train_one_step(model, optimizer, x, y):
# At each train step, first calculate the forward loss
with tf.GradientTape() as tape:
logits = model(x)
loss = compute_loss(logits, y)
# Then calculate the backward gradients over each trainable variables
grads = tape.gradient(loss, model.trainable_variables)
# Optimize and update variables throught backpropagation
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# Compute current model accuracy
accuracy = compute_accuracy(logits, y)
return loss, accuracy
def train(epoch, model, optimizer, trainset):
#def train(epoch, model, optimizer):
# trainset = mnist_dataset()[0]
loss = 0.0
accuracy = 0.0
#for step, (x, y) in enumerate(tqdm(trainset)):
for step, (x, y) in enumerate(tqdm(trainset)):
loss, accuracy = train_one_step(model, optimizer, x, y)
if step % 110 == 0:
print('epoch', epoch, ': loss', loss.numpy(), '; accuracy', accuracy.numpy())
return loss, accuracy
class MyModel(keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.layer1 = layers.Conv2D(filters=32, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME", input_shape=(-1, 28, 28, 1))
self.layer2 = layers.MaxPool2D(pool_size=(2,2), strides=[1,1], padding="SAME")
self.layer3 = layers.Conv2D(filters=64, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME")
self.layer4 = layers.MaxPool2D(pool_size=(2,2), strides=[2,2], padding="SAME")
self.layer5 = layers.Flatten()
self.layer6 = layers.Dense(units=512, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
self.layer7 = layers.Dense(units=10, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
def call(self, x, training=False):
x = tf.reshape(x, (-1, 28, 28, 1))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
return x
def main():
# set random seed
tf.random.set_seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
trainset, evalset = mnist_dataset()
model = MyModel()
optimizer = optimizers.Adam(lr=0.001)
# Save checkpoints with keras api as the first approach
# Save checkpoints manually as a second approach.
# find a way to implement early-stopping strategy in the programming style
# for epoch in tqdm(range(30)):
for epoch in range(50):
loss, accuracy = train(epoch, model, optimizer, trainset)
print('Final epoch', epoch, ': loss', loss.numpy(), '; accuracy', accuracy.numpy())
if __name__ == '__main__':
main()
最后一种方法如下：
import os, sys
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, models, layers, regularizers, optimizers
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
def mnist_dataset():
(x_train, y_train), (x_eval, y_eval) = datasets.mnist.load_data()
print('x_train/y_train shape:', x_train.shape, y_train.shape)
y_train = tf.one_hot(y_train, depth=10)
y_eval = tf.one_hot(y_eval, depth=10)
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
ds_train = ds_train.map(prepare_mnist_features_and_labels)
ds_train = ds_train.shuffle(x_train.shape[0]).batch(128)
ds_eval = tf.data.Dataset.from_tensor_slices((x_eval, y_eval))
ds_eval = ds_eval.map(prepare_mnist_features_and_labels)
ds_eval = ds_eval.shuffle(x_eval.shape[0]).batch(128)
sample = next(iter(ds_train))
print('sample: ', sample[0].shape, sample[1].shape)
return ds_train, ds_eval
class MyModel(keras.Model):
# self.model = keras.Sequential([
# layers.Reshape(target_shape=(28*28, ), input_shape=(28, 28)),
# layers.Dense(100, activation=tf.nn.relu),
# layers.Dense(100, activation=tf.nn.relu),
# layers.Desnse(10)
# ])
def __init__(self):
super(MyModel, self).__init__()
self.layer1 = layers.Conv2D(filters=32, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME", input_shape=(-1, 28, 28, 1))
self.layer2 = layers.MaxPool2D(pool_size=(2,2), strides=[1,1], padding="SAME")
self.layer3 = layers.Conv2D(filters=64, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME")
self.layer4 = layers.MaxPool2D(pool_size=(2,2), strides=[2,2], padding="SAME")
self.layer5 = layers.Flatten()
self.layer6 = layers.Dense(units=512, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
self.layer7 = layers.Dense(units=10, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
def call(self, x, training=False):
x = tf.reshape(x, (-1, 28, 28, 1))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
return x
def main():
tf.random.set_seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
trainset, evalset = mnist_dataset()
model = MyModel()
model.compile(optimizer=optimizers.Adam(lr=0.001), loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
model.fit(trainset.repeat(), epochs=30, steps_per_epoch=500, verbose=1,
validation_data=evalset.repeat(), validation_steps=10)
if __name__ == '__main__':
main()
他们每个人都需要一段时间来训练。谁能告诉我为什么表现不同？还有以后自己怎么调试？
非常感谢您的帮助。
仔细检查网络后问题解决。事实证明，模型中的最后一个全连接层是用 relu 函数激活的，这是不合适的。而损失函数的选择tf.losses.categoricalCrossentropy和tf.nn.sparse_softmax_cross_entropy_with_logits也有很大的区别。无论选择什么，都要确保损失函数和网络的最终输出匹配。
那里。我是初学者，正在学习 Tensorflow 2.0。我有一个模型以 3 种不同的方法调用。而且表现也不同。谁能告诉我为什么会这样？
模型构建调用方法：
import os, sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, models, layers, regularizers, optimizers
def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
def mninst_dataset():
(x_train, y_train), (x_eval, y_eval) = datasets.mnist.load_data()
print('x_train/y_train shape:', x_train.shape, y_train.shape)
y_train = tf.one_hot(y_train, depth=10)
y_eval = tf.one_hot(y_eval, depth=10)
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
ds_train = ds_train.map(prepare_mnist_features_and_labels)
ds_train = ds_train.shuffle(x_train.shape[0]).batch(128)
ds_eval = tf.data.Dataset.from_tensor_slices((x_eval, y_eval))
ds_eval = ds_eval.map(prepare_mnist_features_and_labels)
ds_eval = ds_eval.shuffle(x_eval.shape[0]).batch(128)
sample = next(iter(ds_train))
print('sample: ', sample[0].shape, sample[1].shape)
return ds_train, ds_eval
def main():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
trainset, evalset = mninst_dataset()
model = keras.Sequential()
model.add(layers.Reshape(target_shape=[28, 28, 1], input_shape=[28, 28]))
model.add(layers.Conv2D(filters=32, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME"))
model.add(layers.MaxPool2D(pool_size=(2,2), strides=[1,1], padding="SAME"))
model.add(layers.Conv2D(filters=64, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME"))
model.add(layers.MaxPool2D(pool_size=(2,2), strides=[2,2], padding="SAME"))
model.add(layers.Flatten())
model.add(layers.Dense(units=512, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01)))
model.add(layers.Dense(units=10, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01)))
model.compile(optimizer=optimizers.Adam(lr=0.01), loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
model.fit(trainset.repeat(), epochs=30, steps_per_epoch=500,
validation_data=evalset.repeat(), validation_steps=10)
if __name__=='__main__':
main()
构建模型的第二种方法运行如下：
import os, sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, models, layers, regularizers, optimizers
from tqdm import tqdm
def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
def mnist_dataset():
(x_train, y_train), (x_eval, y_eval) = datasets.mnist.load_data()
# y_train = tf.one_hot(y_train, depth=10)
# y_eval = tf.one_hot(y_eval, depth=10)
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
ds_train = ds_train.map(prepare_mnist_features_and_labels)
# Test: replace x_train.shape[0] by the number of the training samples, which is 60000
ds_train = ds_train.shuffle(x_train.shape[0]).batch(128)
ds_eval = tf.data.Dataset.from_tensor_slices((x_eval, y_eval))
ds_eval = ds_eval.map(prepare_mnist_features_and_labels)
ds_eval = ds_eval.shuffle(x_eval.shape[0]).batch(128)
# sample = next(iter(ds_train))
# print('sample: ', sample[0].shape, sample[1].shape)
return ds_train, ds_eval
# tf.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name=None):
# labels: Tensof of shape [d_0, d_1, ..., d_{r-1}]. Each label must be an index in [0, num_classes]
# logits: Unscaled of log probabilities of shape [d_0, d_1, ..., d_{r-1}, num_classes]
# A common use is to have logits of shape [batch_size, num_classes] and have labels of shape [batch_size]
def compute_loss(logits, labels):
# print(logits.numpy())
# print(labels.numpy())
return tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits
)
)
def compute_accuracy(logits, labels):
predictions = tf.argmax(logits, axis=1)
# print(predictions)
# print(labels)
# print(list(zip(predictions.numpy(), labels.numpy())))
return tf.reduce_mean(tf.cast(tf.equal(predictions, labels), tf.float32))
def train_one_step(model, optimizer, x, y):
# At each train step, first calculate the forward loss
with tf.GradientTape() as tape:
logits = model(x)
loss = compute_loss(logits, y)
# Then calculate the backward gradients over each trainable variables
grads = tape.gradient(loss, model.trainable_variables)
# Optimize and update variables throught backpropagation
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# Compute current model accuracy
accuracy = compute_accuracy(logits, y)
return loss, accuracy
def train(epoch, model, optimizer, trainset):
#def train(epoch, model, optimizer):
# trainset = mnist_dataset()[0]
loss = 0.0
accuracy = 0.0
#for step, (x, y) in enumerate(tqdm(trainset)):
for step, (x, y) in enumerate(tqdm(trainset)):
loss, accuracy = train_one_step(model, optimizer, x, y)
if step % 110 == 0:
print('epoch', epoch, ': loss', loss.numpy(), '; accuracy', accuracy.numpy())
return loss, accuracy
class MyModel(keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.layer1 = layers.Conv2D(filters=32, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME", input_shape=(-1, 28, 28, 1))
self.layer2 = layers.MaxPool2D(pool_size=(2,2), strides=[1,1], padding="SAME")
self.layer3 = layers.Conv2D(filters=64, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME")
self.layer4 = layers.MaxPool2D(pool_size=(2,2), strides=[2,2], padding="SAME")
self.layer5 = layers.Flatten()
self.layer6 = layers.Dense(units=512, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
self.layer7 = layers.Dense(units=10, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
def call(self, x, training=False):
x = tf.reshape(x, (-1, 28, 28, 1))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
return x
def main():
# set random seed
tf.random.set_seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
trainset, evalset = mnist_dataset()
model = MyModel()
optimizer = optimizers.Adam(lr=0.001)
# Save checkpoints with keras api as the first approach
# Save checkpoints manually as a second approach.
# find a way to implement early-stopping strategy in the programming style
# for epoch in tqdm(range(30)):
for epoch in range(50):
loss, accuracy = train(epoch, model, optimizer, trainset)
print('Final epoch', epoch, ': loss', loss.numpy(), '; accuracy', accuracy.numpy())
if __name__ == '__main__':
main()
最后一种方法如下：
import os, sys
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, models, layers, regularizers, optimizers
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def prepare_mnist_features_and_labels(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
def mnist_dataset():
(x_train, y_train), (x_eval, y_eval) = datasets.mnist.load_data()
print('x_train/y_train shape:', x_train.shape, y_train.shape)
y_train = tf.one_hot(y_train, depth=10)
y_eval = tf.one_hot(y_eval, depth=10)
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
ds_train = ds_train.map(prepare_mnist_features_and_labels)
ds_train = ds_train.shuffle(x_train.shape[0]).batch(128)
ds_eval = tf.data.Dataset.from_tensor_slices((x_eval, y_eval))
ds_eval = ds_eval.map(prepare_mnist_features_and_labels)
ds_eval = ds_eval.shuffle(x_eval.shape[0]).batch(128)
sample = next(iter(ds_train))
print('sample: ', sample[0].shape, sample[1].shape)
return ds_train, ds_eval
class MyModel(keras.Model):
# self.model = keras.Sequential([
# layers.Reshape(target_shape=(28*28, ), input_shape=(28, 28)),
# layers.Dense(100, activation=tf.nn.relu),
# layers.Dense(100, activation=tf.nn.relu),
# layers.Desnse(10)
# ])
def __init__(self):
super(MyModel, self).__init__()
self.layer1 = layers.Conv2D(filters=32, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME", input_shape=(-1, 28, 28, 1))
self.layer2 = layers.MaxPool2D(pool_size=(2,2), strides=[1,1], padding="SAME")
self.layer3 = layers.Conv2D(filters=64, kernel_size=(5, 5), activation=tf.nn.relu, strides=[1,1], padding="SAME")
self.layer4 = layers.MaxPool2D(pool_size=(2,2), strides=[2,2], padding="SAME")
self.layer5 = layers.Flatten()
self.layer6 = layers.Dense(units=512, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
self.layer7 = layers.Dense(units=10, activation=tf.nn.relu, kernel_regularizer=regularizers.l2(0.01))
def call(self, x, training=False):
x = tf.reshape(x, (-1, 28, 28, 1))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
return x
def main():
tf.random.set_seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
trainset, evalset = mnist_dataset()
model = MyModel()
model.compile(optimizer=optimizers.Adam(lr=0.001), loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
model.fit(trainset.repeat(), epochs=30, steps_per_epoch=500, verbose=1,
validation_data=evalset.repeat(), validation_steps=10)
if __name__ == '__main__':
main()
他们每个人都需要一段时间来训练。谁能告诉我为什么表现不同？还有以后自己怎么调试？
非常感谢您的帮助。
仔细检查网络后问题解决。事实证明，模型中的最后一个全连接层是用 relu 函数激活的，这是不合适的。而损失函数的选择tf.losses.categoricalCrossentropy和tf.nn.sparse_softmax_cross_entropy_with_logits也有很大的区别。无论选择什么，都要确保损失函数和网络的最终输出匹配。