Can you explain how the feature is extracted from the following code of CNN-CodePudding

How the Image Features are extracted from the following convolutional neural network code

import tensorflow as tf
from tensorflow.keras.utils import img_to_array
df['PubChem_ID'] = df['PubChem_ID'].apply(str)
df_image = []
for i in tqdm(range(df.shape[0])):
    img = image.load_img('/content/drive/MyDrive/3D Conformer/Conformer/' df['PubChem_ID'] 
    [i] '.png',target_size=(256,256,3))
    img = image.img_to_array(img)
    img = img/255
    df_image.append(img)
X = np.array(df_image)

The image is converted into the size 256 x 256 x 3 in matrix with three layers (RGB), where each layer contains 256 x 256 values.

y = np.array(df.drop(['PubChem_ID'],axis=1))


model = Sequential()
model.add(Convolution2D(64, kernel_size=(3, 3),padding='same',input_shape=(256,256,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(29))
model.add(Activation('sigmoid'))

CodePudding user response：

In the given code, a convolutional neural network (CNN) is used to extract image features from a dataset of images. The images in the dataset are first converted to a size of 256 x 256 x 3, where the 3 represents the 3 color channels (red, green, and blue) of the image.

The image features are extracted using the following steps:

The Convolution2D layer applies a set of filters to the input image, each of which is a 3 x 3 matrix of weights. This layer performs a convolution operation on the input image to create a new feature map. The Activation layer applies a non-linear activation function (in this case, the ReLU function) to the output of the Convolution2D layer. This allows the network to learn more complex patterns in the data.

The MaxPooling2D layer performs a max pooling operation on the output of the Activation layer, which reduces the spatial dimensions of the feature map. This helps to reduce the number of parameters in the model and to prevent overfitting.

The Dropout layer randomly sets a fraction of the output values to zero, which helps to prevent overfitting by reducing the dependence on any one feature.

The Flatten layer flattens the output of the Dropout layer into a single vector of values. This allows the output to be fed into the next layer of the network.

The Dense layer applies a linear transformation to the flattened feature vector, which produces a 29-dimensional output vector. This layer represents the final set of image features extracted by the network.

The Activation layer applies the sigmoid activation function to the output of the Dense layer, which produces a final output vector of probabilities. This output can be used for classification or other tasks.

Overall, the given code uses a CNN to extract a set of 29 image features from the input images. These features are learned by the network during training and can be used to represent the visual content of the images in a compact and useful form.