我想在 Web 应用程序中使用我的深度学习 CNN 算法，并且我使用终端 tensorflowjs_conv 中的这条命令将我的 tensorflow 模型 emotion_model.h5 转换为 tensorflow.js 模型...
我想在 Web 应用程序中使用我的深度学习 CNN 算法，并且我使用终端中的此命令将我的 tensorflow 模型 emotion_model.h5 转换为 tensorflow.js 模型
tensorflowjs_converter --input_format keras models/emotion_model.h5 models/tfjs_model 然而，当我在 Web 应用程序中使用 tfjs_model 对情绪进行分类时，控制台上出现以下错误：
“未捕获（在承诺中）错误：应向 InputLayer 传递 batchInputShape 或 inputShape”
请注意，我在模型训练期间有一个明确定义的 input_shape 参数，并且我的 tensorflow 模型运行良好。以下是我的模型层的摘要。
emotion_model = tf.keras.Sequential()
emotion_model.add(tf.keras.layers.Input(shape=(48, 48, 1)))
emotion_model.add(tf.keras.layers.Conv2D(32, kernel_size=(3,3), activation = 'relu'))
emotion_model.add(tf.keras.layers.Conv2D(64, kernel_size=(3,3), activation = 'relu'))
emotion_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
emotion_model.add(tf.keras.layers.Dropout(0.25))
emotion_model.add(tf.keras.layers.Conv2D(128, kernel_size=(3,3), activation = 'relu'))
emotion_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
emotion_model.add(tf.keras.layers.Conv2D(128, kernel_size=(3,3), activation = 'relu'))
emotion_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
emotion_model.add(tf.keras.layers.Dropout(0.25))
emotion_model.add(tf.keras.layers.Flatten())
emotion_model.add(tf.keras.layers.Dense(1024, activation='relu'))
emotion_model.add(tf.keras.layers.Dropout(0.5))
emotion_model.add(tf.keras.layers.Dense(7, activation='relu'))
emotion_model.compile(loss='categorical_crossentropy', optimizer= keras.optimizers.Adam(learning_rate= 0.0001, weight_decay=1e-6), metrics=['accuracy'])
我目前正在使用 tensorflow==16.0.2。我在网上找到了类似的查询，在切换到旧版本的 tensorflow（tensorflow==15.0.1）后，它们的错误得到了解决。我尝试卸载 tensorflow 16.0.2 并安装 tensorflow 15.0.1，但我的查询仍然没有得到解决。我不想在 tensorflow.js 中训练我的整个模型。我该怎么做才能解决这个错误？
我正在开发一个 Web 应用程序，该应用程序使用 AWS Rekognition 根据上传的图像预测健身器材。图像上传和预测工作正常，但我无法显示预测结果...
我正在开发一个 Web 应用程序，该应用程序使用 AWS Rekognition 根据上传的图像预测健身器材。图像上传和预测工作正常，但我无法在单独的 HTML 页面上显示预测结果。
homepage.html：允许用户上传图像并将其发送到 AWS Lambda 函数进行预测。results.html：应显示预测结果。
即使预测结果正确记录在浏览器的控制台中，results.html 页面也会显示“未找到设备”而不是实际预测。
homepage.html 的代码：
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Gym Equipment Exercise Finder</title>
<style>
/* Your existing CSS here */
</style>
</head>
<body>
<header>
<h1 style="font-family: Times New Roman, serif">Workout Wizard</h1>
<nav>
<a href="homepage.html">Home</a>
<a href="products.html">Products</a>
<a href="contact.html">Contact</a>
</nav>
</header>
<section class="hero">
<div class="hero-content">
<h1>Find Your Workout!</h1>
<p>Upload a photo of gym equipment and get exercises you can do with it.</p>
</div>
</section>
<section class="upload-section">
<div class="upload-container">
<input type="file" id="imageInput" accept="image/*">
<label for="imageInput">
<img src="camera.png" alt="Upload Icon">
<p>Drop an image here</p>
<p>or</p>
<p>click to browse</p>
</label>
</div>
<button onclick="uploadImage()">Submit</button>
</section>
<footer>
© 2024 Workout Wizard. All rights reserved.
</footer>
<script>
function uploadImage() {
const imageInput = document.getElementById('imageInput');
if (!imageInput || !imageInput.files || imageInput.files.length === 0) {
alert("Please choose an image file.");
return;
}
const selectedFile = imageInput.files[0];
const reader = new FileReader();
reader.onload = function (event) {
const base64EncodedImage = event.target.result.split(',')[1];
fetch('https://t6mn1ryxc4.execute-api.us-east-1.amazonaws.com/Gym_Equipments/predict', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ image: base64EncodedImage }),
})
.then(response => response.json())
.then(data => {
console.log('Lambda Response:', data);
localStorage.setItem('predictedEquipment', data);
console.log('Stored in localStorage:', localStorage.getItem('predictedEquipment'));
window.location.href = "results.html";
})
.catch(error => {
console.error('Error:', error);
alert('Error uploading image.');
});
};
reader.readAsDataURL(selectedFile);
}
</script>
</body>
</html>
results.html 的代码：
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Results</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 0;
background-color: #f3f4f6;
}
header {
background-color: #6200a9;
padding: 20px;
display: flex;
justify-content: space-between;
align-items: center;
color: white;
}
header img {
height: 50px;
}
nav {
display: flex;
align-items: center;
}
nav a {
color: white;
text-decoration: none;
margin: 0 10px;
font-size: 18px;
}
nav a:hover {
text-decoration: underline;
}
.result-section {
padding: 20px;
text-align: center;
background-color: #fff;
border-radius: 10px;
margin: 20px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}
footer {
background-color: #6200a9;
color: #ccc;
padding: 20px;
text-align: center;
}
footer a {
color: #ff7f50;
text-decoration: none;
}
footer a:hover {
text-decoration: underline;
}
</style>
</head>
<body>
<header>
<h1>Workout Wizard</h1>
<nav>
<a href="homepage.html">Home</a>
<a href="products.html">Products</a>
<a href="contact.html">Contact</a>
</nav>
</header>
<section class="result-section">
<h1>Prediction Result</h1>
<p id="result">Loading...</p>
</section>
<footer>
© 2024 Workout Wizard. All rights reserved. | <a href="privacy.html">Privacy Policy</a> | <a href="termsandconditions.html">Terms of Service</a>
</footer>
<script>
window.onload = function() {
// Retrieve the prediction result from localStorage
const prediction = localStorage.getItem('predictedEquipment');
if (prediction) {
document.getElementById('result').innerText = prediction;
} else {
document.getElementById('result').innerText = 'No equipment predicted';
}
};
</script>
</body>
</html>
lambda 函数：
import json
import boto3
bucket_name = "gymhtml"
from botocore.exceptions import ClientError
import base64
from io import BytesIO
from datetime import datetime
s3 = boto3.client('s3')
def saveToS3(image_binary):
# Get the current timestamp
timestamp = datetime.utcnow().strftime("%Y%m%d%H%M%S%f")
# Generate a filename with the timestamp
filename = f'uploads/{timestamp}.jpg'
print("filename: ", filename)
try:
# need to save the image as image/jpeg content type
s3.put_object(Bucket=bucket_name, Key=filename, Body=image_binary, ContentType='image/jpeg')
# Log success message
print(f"Image saved to S3: s3://{bucket_name}/{filename}")
return filename
except ClientError as e:
# Log the error
print("Error:", str(e))
return None
def predictImage(photo):
rekognition = boto3.client('rekognition')
model = "arn:aws:rekognition:us-east-1:414942631188:project/gymequipment/version/gymequipment.2024-07-31T18.03.52/1722420232922"
min_confidence = 60
#Call DetectCustomLabels
response = rekognition.detect_custom_labels(Image={'S3Object': {'Bucket': bucket_name, 'Name': photo}},
MinConfidence=min_confidence,
ProjectVersionArn=model)
print(response['CustomLabels'])
result = json.dumps(response['CustomLabels'][0])
#print to the log for debugging purposes
print(response['CustomLabels'][0]["Name"])
return response['CustomLabels'][0]["Name"]
def lambda_handler(event, context):
# TODO implement
msg = "INVALID REQUEST"
print( event );
#if event['httpMethod'] == 'POST':
# Extract the base64-encoded image and filename from the request body
#request_body = json.loads(event['body'])
request_body = json.loads(event['body'])
image_base64 = request_body['image'];
# Decode the base64-encoded image
image_binary = base64.b64decode(image_base64)
#save image to S3
imagefilename = saveToS3(image_binary)
#msg = 'Hello from Lambda!'
#msg = predictImage(imagefilename)
msg = predictImage(imagefilename)
return {
'statusCode': 200,
'headers': {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*'
},
'body': json.dumps(msg)
}
您向我们展示了很多代码。您能把事情简化成其他人可以尝试并自己重现的东西吗？另外，您提到它显示“未找到设备”，但似乎没有任何代码会生成此消息。有关提出好问题的提示，请参阅：