您好，我正在使用 ResNet152V2 做一个关于 TensorFlow 图像分类的小项目。
我编写了一个
Train-Predict.py
脚本，它能够训练
trained_weights.hdf5
文件以成功预测自闭症和非自闭症人士的图像。
此处。是脚本：
#Import Libraries
import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import models
from tensorflow.keras.applications import ResNet152V2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dropout, Dense, BatchNormalization, GlobalAveragePooling2D, Conv2D
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.optimizers import Adam
from PIL import Image
import cv2
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
img_size = 224
batchsize = 128
epochs = 50
lrate = 0.01
lrate_reduction_factor = 0.5
training = False
#Variable setup and Detect images/Classes in dataset folders
traindatadir="Train"
testdatadir="Test"
#Data Augmentation
datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
vertical_flip = True,
rotation_range=15,
shear_range=0.1,
zoom_range=0.2,
width_shift_range=0.1,
height_shift_range=0.1
)
#Preprosessing Data
train_datagen=datagen.flow_from_directory(
traindatadir,
target_size = (img_size, img_size),
color_mode = 'rgb',
batch_size = batchsize,
shuffle = True,
seed = 123,
class_mode= 'categorical'
)
test_datagen=datagen.flow_from_directory(
testdatadir,
target_size = (img_size, img_size),
color_mode = 'rgb',
batch_size = batchsize,
shuffle = True,
seed = 123,
class_mode= 'categorical'
)
#ResNet Model with custom node tuning (Reduced number of Nodes this time)
resnet = ResNet152V2(
include_top = False,
weights = 'imagenet',
input_shape = (img_size, img_size, 3)
)
resnet.trainable = False
model = Sequential()
model.add(resnet)
model.add(Conv2D(512, kernel_size=(3,3), activation="relu"))
model.add(Conv2D(512, kernel_size=(3,3), activation="relu"))
model.add(Conv2D(512, kernel_size=(3,3), activation="relu"))
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dense(64, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(128, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=lrate), loss = 'categorical_crossentropy', metrics = ['accuracy'])
#model.compile(loss='categorical_crossentropy', optimizer="Adam", metrics=['accuracy'])
model.summary()
#Use ReduceLR to reduce learning rate when metric not improving
earlystop = EarlyStopping(patience=20)
learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss',
patience=10,
verbose=1,
factor=lrate_reduction_factor,
min_lr=0.000000000000001)
callbacks = [earlystop, learning_rate_reduction]
if training:
history = model.fit(train_datagen,epochs=epochs,batch_size=batchsize,validation_data=test_datagen,callbacks=callbacks)
model.save('trained_weights.hdf5')
#Plot
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6,6))
ax1.plot(history.history['loss'], color='b', label="Training loss")
ax1.plot(history.history['val_loss'], color='r', label="validation loss")
ax1.set_xticks(np.arange(0, epochs, (epochs/10)))
ax1.legend()
ax2.plot(history.history['accuracy'], color='b', label="Training accuracy")
ax2.plot(history.history['val_accuracy'], color='r',label="Validation accuracy")
ax2.set_xticks(np.arange(0, epochs, (epochs/10)))
ax2.legend()
legend = plt.legend(loc='best', shadow=True)
plt.tight_layout()
plt.show()
#Model Prediction
model = models.load_model('trained_weights.hdf5', compile = True)
predict_path = "Train"
datagen = ImageDataGenerator(
rescale = 1./255,
)
predict_data = datagen.flow_from_directory(
predict_path,
target_size = ((img_size,img_size)),
)
ci = predict_data.class_indices
classes = {v: k for k, v in ci.items()}
#path = "C:/Users/J.A.X/Desktop/TempEnv/Test/autistic/1028.jpg"
path = "C:/Users/J.A.X/Desktop/TempEnv/Train/non_autistic/0001.jpg"
#path = input('Please enter path of image to classify: \n')
inp = Image.open(path)
img = inp.resize((img_size,img_size))
img = np.array(img)/255.0
img = np.reshape(img, [1,img_size,img_size,3])
predictions = model.predict(img)
top_values, top_indices = tf.nn.top_k(predictions, k=2)
values = np.array(top_values)
indices = np.array(top_indices)
#print('Input Image: \n\n\n')
#inp.show()
print('Probabilities: \n')
#print(values)
#print(indices)
for i in range(2):
print(classes[indices[0][i]] + " : ", end = "")
print(values[0][i] * 100)
print()
image = cv2.imread(path)
# Convert the image from BGR to RGB
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (img_size, img_size))
# Expand dimensions to match the expected input shape (1, 224, 224, 3)
image = np.expand_dims(image, axis=0)
# Convert image to float32 and normalize
image = image.astype(np.float32) / 255.0
# checking how it looks
plt.imshow(image[0]) # Note: image[0] because image now has a batch dimension
plt.show()
print(image.shape) # Print Shape
i = np.argmax(predictions[0])
print(i) # 0 is autistic and 1 is non austistic
输出：
Found 2054 images belonging to 2 classes.
Found 882 images belonging to 2 classes.
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
resnet152v2 (Functional) (None, 7, 7, 2048) 58331648
conv2d (Conv2D) (None, 5, 5, 512) 9437696
conv2d_1 (Conv2D) (None, 3, 3, 512) 2359808
conv2d_2 (Conv2D) (None, 1, 1, 512) 2359808
global_average_pooling2d (G (None, 512) 0
lobalAveragePooling2D)
dropout (Dropout) (None, 512) 0
dense (Dense) (None, 512) 262656
dense_1 (Dense) (None, 64) 32832
dropout_1 (Dropout) (None, 64) 0
dense_2 (Dense) (None, 256) 16640
batch_normalization (BatchN (None, 256) 1024
ormalization)
dense_3 (Dense) (None, 128) 32896
dense_4 (Dense) (None, 2) 258
=================================================================
Total params: 72,835,266
Trainable params: 14,503,106
Non-trainable params: 58,332,160
_________________________________________________________________
Found 2054 images belonging to 2 classes.
1/1 [==============================] - 3s 3s/step
Probabilities:
non_autistic : 81.39994740486145
autistic : 18.60005408525467
(1, 224, 224, 3)
1
我希望创建一个热图来可视化卷积层中发生激活的位置。
类似这样的东西：
但是在在线跟踪数十个 Grad-Cam 示例之后并编写几十个函数，例如：
# Grad-CAM implementation
def get_img_array(img_path, size):
img = load_img(img_path, target_size=size)
array = np.expand_dims(np.array(img), axis=0)
return array / 255.0
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
grad_model = tf.keras.models.Model(
[model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
)
with tf.GradientTape() as tape:
last_conv_layer_output, preds = grad_model(img_array)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index]
grads = tape.gradient(class_channel, last_conv_layer_output)
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
return heatmap.numpy()
def save_and_display_gradcam(img_path, heatmap, cam_path="cam.jpg", alpha=0.4):
img = cv2.imread(img_path)
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
superimposed_img = heatmap * alpha + img
cv2.imwrite(cam_path, superimposed_img)
img = cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.axis('off')
plt.show()
img_array = get_img_array(path, size=(img_size, img_size))
# Generate Grad-CAM heatmap
last_conv_layer_name = "conv5_block3_out"
heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)
# Display Grad-CAM
save_and_display_gradcam(path, heatmap)
每个不同的示例都会导致不同的
错误
各种类型，但大多数都指向
resnet
嵌套在实际模型中...
根据我读过的示例表示使用模型的最后一个卷积层。不太确定它们到底意味着 resnet 中的
"conv5_block3_out"
层还是 resnet 之外的模型中的我的
conv2d_2
卷积层...
欢迎对编写和显示正确的热图有任何帮助。我愿意在这一点上尝试任何事情。
虚拟环境是 minicoda
设置虚拟环境的说明：
conda create --name Cuda_Python3.8 python=3.8 -y
conda activate Cuda_Python3.8
conda install cudatoolkit=11.2 cudnn=8.1 -c=conda-forge -y
pip install tensorflow-gpu==2.10.1
pip install spyder==5.5.5
pip install Pillow==10.4.0
pip install matplotlib==3.7.5
pip install opencv-python==4.10.0.84
你遇到的问题在于你的 Grad-CAM 实现尝试访问 ResNet152V2 模型内部的层，而你之前已经冻结了这些层的训练。 为了解决这个问题，你需要在 Grad-CAM 中使用你的自定义模型的最后一个卷积层，而不是 ResNet 的。
以下是更新后的代码，其中包含必要的更改：
# ... (你之前的代码) ...
# Grad-CAM implementation
def get_img_array(img_path, size):
img = load_img(img_path, target_size=size)
array = np.expand_dims(np.array(img), axis=0)
return array / 255.0
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
grad_model = tf.keras.models.Model(
[model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
)
with tf.GradientTape() as tape:
last_conv_layer_output, preds = grad_model(img_array)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index]
grads = tape.gradient(class_channel, last_conv_layer_output)
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
return heatmap.numpy()
def save_and_display_gradcam(img_path, heatmap, cam_path="cam.jpg", alpha=0.4):
img = cv2.imread(img_path)
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
superimposed_img = heatmap * alpha + img
cv2.imwrite(cam_path, superimposed_img)
img = cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.axis('off')
plt.show()
img_array = get_img_array(path, size=(img_size, img_size))
# **修改：使用你自定义模型的最后一个卷积层的名称**
last_conv_layer_name = "conv2d_2"
heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)
# Display Grad-CAM
save_and_display_gradcam(path, heatmap)
解释:
-
修改: 我们将
last_conv_layer_name从 "conv5_block3_out" 更改为 "conv2d_2"。这是因为 "conv2d_2" 是你自定义模型中最后一个卷积层的名称。 -
其他的 Grad-CAM 函数保持不变，因为它们现在将使用正确的卷积层来生成热图。
通过这个修改，你的 Grad-CAM 实现应该可以正常工作，并生成你期望的热图，突出显示图像中对分类决策重要的区域.
标签：python,tensorflow,conv-neural-network,resnet,image-classification From： 78773167