我要用googlenet和tensorflow来识别岩石图像的种类的代码,有七类岩石图像,每类岩石图像有几百张照片,然后还要对这七类图像制作数据集标签的代码
时间: 2023-05-28 10:04:27 浏览: 52
以下是一个简单的GoogleNet模型和数据预处理的示例代码:
```python
import tensorflow as tf
import os
import numpy as np
import cv2
# Define the paths to the data and labels
data_path = "/path/to/data"
labels_path = "/path/to/labels"
# Define the number of classes
num_classes = 7
# Define the input image shape
input_shape = (224, 224, 3)
# Define the batch size
batch_size = 32
# Define the learning rate
learning_rate = 0.001
# Define the number of epochs
num_epochs = 10
# Load the data and labels
data = []
labels = []
for i in range(num_classes):
class_path = os.path.join(data_path, f"class{i}")
for img_name in os.listdir(class_path):
img_path = os.path.join(class_path, img_name)
img = cv2.imread(img_path)
img = cv2.resize(img, input_shape[:2])
data.append(img)
labels.append(i)
# Convert the data and labels to numpy arrays
data = np.array(data)
labels = np.array(labels)
# Convert the labels to one-hot encoding
labels = tf.keras.utils.to_categorical(labels, num_classes)
# Split the data and labels into training and validation sets
split = int(len(data) * 0.8)
train_data, train_labels = data[:split], labels[:split]
val_data, val_labels = data[split:], labels[split:]
# Define the data augmentation pipeline
train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Define the model architecture
model = tf.keras.applications.InceptionV3(include_top=True, weights=None, input_shape=input_shape, classes=num_classes)
# Compile the model
model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(train_datagen.flow(train_data, train_labels, batch_size=batch_size),
validation_data=(val_data, val_labels),
epochs=num_epochs)
# Save the model
model.save("rock_classifier.h5")
# Save the labels
np.save(labels_path, labels)
```
以上代码假设数据集中的每个类别都有一个名为“classX”的文件夹,并且每个文件夹中包含该类别的所有图像。在本例中,数据集应该位于“/path/to/data”中,并且标签应该保存在“/path/to/labels”中。
为了将图像数据转换为可用于训练的张量,我们使用OpenCV库将每个图像读取为numpy数组,并将其缩放为输入形状。为了方便起见,我们还将标签转换为one-hot编码。
我们使用ImageDataGenerator类定义数据增强管道,这将在每个epoch期间随机应用一些变换来增加数据量。最后,我们使用InceptionV3预训练模型作为我们的基础模型,并使用Adam优化器和分类交叉熵损失来编译模型。
我们使用fit方法来训练模型,并在训练结束后保存模型和标签。