我刚接触Tensorflow和Keras。为了入门，我按照教程https://www.tensorflow.org/tutorials/quickstart/advanced进行学习。现在我正在尝试将其适配到CIFAR10数据集上，而不是MNIST数据集上。我重新创建了这个模型https://keras.io/examples/cifar10_cnn/，并试图在自己的代码库中运行它。
逻辑上讲，如果模型、批次大小和优化器都相同，那么两者应该执行相同，但实际上并非如此。我认为可能是我在准备数据方面犯了错误。因此，我将keras代码中的model.fit函数复制到我的脚本中，并且仍然表现更好。使用.fit给出了大约75%的准确率，在25个周期内，而手动方法需要大约60个周期。使用.fit我也实现了稍微更好的最大准确率。
我想知道的是：.fit是否在幕后做了一些优化训练的操作？我需要添加什么来使我的代码达到相同的性能呢？我是否明显地做错了什么？
谢谢你花时间看这个问题。
主要代码:
逻辑上讲，如果模型、批次大小和优化器都相同，那么两者应该执行相同，但实际上并非如此。我认为可能是我在准备数据方面犯了错误。因此，我将keras代码中的model.fit函数复制到我的脚本中，并且仍然表现更好。使用.fit给出了大约75%的准确率，在25个周期内，而手动方法需要大约60个周期。使用.fit我也实现了稍微更好的最大准确率。
我想知道的是：.fit是否在幕后做了一些优化训练的操作？我需要添加什么来使我的代码达到相同的性能呢？我是否明显地做错了什么？
谢谢你花时间看这个问题。
主要代码:
import tensorflow as tf
from tensorflow import keras
import msvcrt
from Plotter import Plotter
#########################Configuration Settings#############################
BatchSize = 32
ModelName = "CifarModel"
############################################################################
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
print("x_train",x_train.shape)
print("y_train",y_train.shape)
print("x_test",x_test.shape)
print("y_test",y_test.shape)
x_train, x_test = x_train / 255.0, x_test / 255.0
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
train_ds = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).batch(BatchSize)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(BatchSize)
loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.0001,decay=1e-6)
# Create an instance of the model
model = ModelManager.loadModel(ModelName,10)
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.CategoricalAccuracy(name='test_accuracy')
########### Using this function I achieve better results ##################
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=BatchSize,
epochs=100,
validation_data=(x_test, y_test),
shuffle=True,
verbose=2)
############################################################################
########### Using the below code I achieve worse results ##################
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images, training=True)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(labels, predictions)
@tf.function
def test_step(images, labels):
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
epoch = 0
InterruptLoop = False
while InterruptLoop == False:
#Shuffle training data
train_ds.shuffle(1000)
epoch = epoch + 1
# Reset the metrics at the start of the next epoch
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()
for images, labels in train_ds:
train_step(images, labels)
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)
test_accuracy = test_accuracy.result() * 100
train_accuracy = train_accuracy.result() * 100
#Print update to console
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print(template.format(epoch,
train_loss.result(),
train_accuracy ,
test_loss.result(),
test_accuracy))
# Check if keyboard pressed
while msvcrt.kbhit():
char = str(msvcrt.getch())
if char == "b'q'":
InterruptLoop = True
print("Stopping loop")
这个模型：
from tensorflow.keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPool2D
from tensorflow.keras import Model
class ModelData(Model):
def __init__(self,NumberOfOutputs):
super(ModelData, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu', padding='same', input_shape=(32,32,3))
self.conv2 = Conv2D(32, 3, activation='relu')
self.maxpooling1 = MaxPool2D(pool_size=(2,2))
self.dropout1 = Dropout(0.25)
############################
self.conv3 = Conv2D(64,3,activation='relu',padding='same')
self.conv4 = Conv2D(64,3,activation='relu')
self.maxpooling2 = MaxPool2D(pool_size=(2,2))
self.dropout2 = Dropout(0.25)
############################
self.flatten = Flatten()
self.d1 = Dense(512, activation='relu')
self.dropout3 = Dropout(0.5)
self.d2 = Dense(NumberOfOutputs,activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.maxpooling1(x)
x = self.dropout1(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.maxpooling2(x)
x = self.dropout2(x)
x = self.flatten(x)
x = self.d1(x)
x = self.dropout3(x)
x = self.d2(x)
return x