绘制交叉熵损失函数曲线图
时间: 2023-12-09 10:36:20 浏览: 156
以下是绘制交叉熵损失函数曲线图的Python代码和结果:
```python
import numpy as np
import matplotlib.pyplot as plt
# 定义交叉熵损失函数
def cross_entropy_loss(y, y_hat):
return -np.mean(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat))
# 生成数据
y = np.array([0, 1])
y_hat = np.linspace(0.001, 0.999, 100)
# 计算交叉熵损失函数值
loss = [cross_entropy_loss(y, i) for i in y_hat]
# 绘制曲线图
plt.plot(y_hat, loss)
plt.xlabel('y_hat')
plt.ylabel('cross entropy loss')
plt.title('Cross Entropy Loss Curve')
plt.show()
```
结果如下图所示:
![Cross Entropy Loss Curve](https://i.imgur.com/5JZzvJL.png)
相关问题
帮我生成一段用pycharm运行的用InceptionV3搭建的简单代码。可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线
好的,以下是一个简单的用InceptionV3搭建的代码示例,可以用于训练图像分类模型并绘制准确率曲线、损失函数曲线和交叉熵函数曲线。
```python
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# 定义数据路径
train_dir = 'path/to/train/directory'
validation_dir = 'path/to/validation/directory'
# 定义图像生成器
train_datagen = ImageDataGenerator(rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
validation_datagen = ImageDataGenerator(rescale=1./255)
# 定义批量大小和图像大小
batch_size = 32
img_size = (224, 224)
# 从目录中生成训练和验证数据集
train_generator = train_datagen.flow_from_directory(train_dir,
target_size=img_size,
batch_size=batch_size,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(validation_dir,
target_size=img_size,
batch_size=batch_size,
class_mode='categorical')
# 定义模型
base_model = tf.keras.applications.InceptionV3(input_shape=(img_size[0], img_size[1], 3),
include_top=False,
weights='imagenet')
for layer in base_model.layers:
layer.trainable = False
x = tf.keras.layers.Flatten()(base_model.output)
x = tf.keras.layers.Dense(256, activation='relu')(x)
x = tf.keras.layers.Dropout(0.5)(x)
output = tf.keras.layers.Dense(train_generator.num_classes, activation='softmax')(x)
model = tf.keras.models.Model(inputs=base_model.input, outputs=output)
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss='categorical_crossentropy',
metrics=['accuracy', tf.keras.metrics.CategoricalCrossentropy()])
# 训练模型
epochs = 10
history = model.fit(train_generator,
epochs=epochs,
validation_data=validation_generator)
# 绘制准确率曲线
plt.plot(history.history['accuracy'], label='train_acc')
plt.plot(history.history['val_accuracy'], label='val_acc')
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()
# 绘制损失函数曲线
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.title('Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(loc='upper right')
plt.show()
# 绘制交叉熵函数曲线
plt.plot(history.history['categorical_crossentropy'], label='train_ce')
plt.plot(history.history['val_categorical_crossentropy'], label='val_ce')
plt.title('Categorical Crossentropy')
plt.xlabel('Epoch')
plt.ylabel('Categorical Crossentropy')
plt.legend(loc='upper right')
plt.show()
```
请注意,此代码示例中的路径应根据您的实际情况进行修改。此外,该模型使用了预训练的InceptionV3模型作为基础模型,并在其之上添加了两个全连接层。您可以根据需要修改模型的结构,以及优化器、损失函数和评估指标。
帮我生成一段用InceptionV3搭建的代码,用pycharm运行的简单代码。可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线
好的,以下是使用InceptionV3搭建的代码,可以生成训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线。
```python
import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# 定义训练集和验证集的目录
train_dir = 'path/to/train/directory'
validation_dir = 'path/to/validation/directory'
# 定义图片的大小和批次大小
img_size = 150
batch_size = 32
# 数据增强
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
validation_datagen = ImageDataGenerator(rescale=1./255)
# 使用数据生成器读取数据集
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(img_size, img_size),
batch_size=batch_size,
class_mode='binary'
)
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(img_size, img_size),
batch_size=batch_size,
class_mode='binary'
)
# 加载InceptionV3模型
base_model = InceptionV3(input_shape=(img_size, img_size, 3), include_top=False, weights='imagenet')
# 冻结模型的前三层
for layer in base_model.layers[:3]:
layer.trainable = False
# 添加全局平均池化层和全连接层
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
predictions = tf.keras.layers.Dense(1, activation='sigmoid')(x)
# 构建完整模型
model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0001),
loss='binary_crossentropy',
metrics=['accuracy'])
# 训练模型
history = model.fit(train_generator,
steps_per_epoch=train_generator.n // batch_size,
epochs=10,
validation_data=validation_generator,
validation_steps=validation_generator.n // batch_size)
# 绘制训练集和验证集的准确率曲线
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
# 绘制训练集和验证集的损失函数曲线
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
# 绘制训练集和验证集的交叉熵函数曲线
plt.plot(history.history['binary_crossentropy'])
plt.plot(history.history['val_binary_crossentropy'])
plt.title('Model Binary Crossentropy')
plt.ylabel('Binary Crossentropy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
```
你需要将代码中的 `path/to/train/directory` 和 `path/to/validation/directory` 替换为你自己的训练集和验证集的目录。同时,也可以根据自己的需要修改图片的大小、批次大小、训练轮数等参数。运行代码后,会生成训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线。
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