A neural network is just a slightly more advanced implementation of machine learning and we call that deep learning
#note
#note
Start with the imports. Here, you are importing TensorFlow and calling it
You then import a library called numpy which helps to represent data as arrays easily and to optimize numerical operations.
tf for convention and ease of use. The framework you will use to build a neural network as a sequence of layers is called keras and is contained within tensorflow, so you can access it using tf.keras.You then import a library called numpy which helps to represent data as arrays easily and to optimize numerical operations.
import tensorflow as tf
import numpy as np
Before creating a neural network you will generate the data that you will be working with. In this case, you are taking 6 X's and 6 Y's. You can see that the relationship between these is y=2x-1, so where x=-1, y=-3 etc.
The standard way of declaring model inputs and outputs is to use
The standard way of declaring model inputs and outputs is to use
numpy, a Python library that provides lots of array type data structures. You can specify these values by building numpy arrays with np.array(). Later in this course, you will learn how to use built-in TensorFlow functions to work with inputs.# Declare model inputs and outputs for training
xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)
Define and Compile the Neural Network
Next, you will create the simplest possible neural network. It has 1 layer with 1 neuron. You will build this model using Keras'
It is good practice to define the expected shape of the input to the model. In this case, you see that each element in xs is a scalar, which you can also treat as a 1-dimensional vector. You can define this shape through the
Next, you will create the simplest possible neural network. It has 1 layer with 1 neuron. You will build this model using Keras'
Sequential class which allows you to define the network as a sequence of layers. You can use a single Dense layer to build this simple network.It is good practice to define the expected shape of the input to the model. In this case, you see that each element in xs is a scalar, which you can also treat as a 1-dimensional vector. You can define this shape through the
tf.keras.Input() object and its shape parameter as shown below. Take note that this is not a layer so even if there are two lines of code inside Sequential below, this is still a one-layer model.# Build a simple Sequential model
model = tf.keras.Sequential([
# Define the input shape
tf.keras.Input(shape=(1,)),
# Add a Dense layer
tf.keras.layers.Dense(units=1)
])
The process of training the neural network, where it 'learns' the relationship between the x's and y's is in the
# Train the model
model.fit() call. This is where it will go through the loop we spoke about above: making a guess, measuring how good or bad it is (aka the loss), using the optimizer to make another guess etc. It will do it for the number of epochs you specify. When you run this code, you'll see the loss on the right hand side.# Train the model
model.fit(xs, ys, epochs=500)
Recognition-Driven Fine-Tuning: Fine-tune an existing low-light enhancement model to better preserve features useful for recognition.
Adaptive Pre-processing: Develop a small, adaptive module that pre-processes low-light images before feeding them to a standard face recognition model.
Adaptive Pre-processing: Develop a small, adaptive module that pre-processes low-light images before feeding them to a standard face recognition model.
My projects
Recognition-Driven Fine-Tuning: Fine-tune an existing low-light enhancement model to better preserve features useful for recognition. Adaptive Pre-processing: Develop a small, adaptive module that pre-processes low-light images before feeding them to a standard…
we are gonna build "Adaptive Pre-Processing" module
I. The Core Idea: Recognition-Focused Pre-processing
Instead of trying to enhance the entire low-light image for general visual appeal, I'll create a small neural network that intelligently transforms the low-light input specifically to make it easier for a face recognition model to work. Think of it as a "low-light image translator" for face recognition.
Instead of trying to enhance the entire low-light image for general visual appeal, I'll create a small neural network that intelligently transforms the low-light input specifically to make it easier for a face recognition model to work. Think of it as a "low-light image translator" for face recognition.
(venv) PS D:\pro-model> python src/dataset_generator.py
Starting dataset generation from: data\raw\lfw-deepfunneled
Saving processed data to: data\processed
Processing persons: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5749/5749 [05:56<00:00, 16.14it/s]
Dataset generation complete. Metadata saved to data\processed\metadata.csv
Generated 26466 pairs of (original, low-light) images.
Starting dataset generation from: data\raw\lfw-deepfunneled
Saving processed data to: data\processed
Processing persons: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5749/5749 [05:56<00:00, 16.14it/s]
Dataset generation complete. Metadata saved to data\processed\metadata.csv
Generated 26466 pairs of (original, low-light) images.
Forwarded from StartUpper
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Forwarded from Ilmiy darajali kadrlarni tayyorlash
Oddiy bo‘ling. Hech qachon o’zingizni boshqalardan ustun qo’ymang. Martabangiz vaqtinchalik. Insoniylik abadiydir.
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Forwarded from Ilmiy darajali kadrlarni tayyorlash
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+------------------------------------------------------------------+
| Low-Light Face Recognition System |
|------------------------------------------------------------------|
| Camera Feed (Original) | Enhanced Face |
| --------------------------- | ------------------------------- |
| | | | |
| | | | |
| | Live Camera | | Enhanced Output |
| | | | |
| | | | |
| --------------------------- | ------------------------------- |
| |
| Status: Identified as Jasurbek Abdullayev (0.83) |
+------------------------------------------------------------------+
═══════════════════════════════════════════════════
IAAL-V3 TEST SET NATIJALARI
═══════════════════════════════════════════════════
LSPI = 1.0000 (5 faol bin)
Bin Aniqlik Og'irlik Namuna
pitch_dark 1.0000 0.9950 18
dark 1.0000 0.8716 19
dim 1.0000 0.4902 27
low 1.0000 0.1751 13
moderate 1.0000 0.0561 20
CMC: Rank-1=1.0000 Rank-5=1.0000
Rank-10=1.0000 Rank-20=1.0000
Grafiklar: reports/lspi_bar.png
reports/cmc_curve.png
═══════════════════════════════════════════════════
oxirida qo'shaman dh ga. Jadval
IAAL-V3 TEST SET NATIJALARI
═══════════════════════════════════════════════════
LSPI = 1.0000 (5 faol bin)
Bin Aniqlik Og'irlik Namuna
pitch_dark 1.0000 0.9950 18
dark 1.0000 0.8716 19
dim 1.0000 0.4902 27
low 1.0000 0.1751 13
moderate 1.0000 0.0561 20
CMC: Rank-1=1.0000 Rank-5=1.0000
Rank-10=1.0000 Rank-20=1.0000
Grafiklar: reports/lspi_bar.png
reports/cmc_curve.png
═══════════════════════════════════════════════════
oxirida qo'shaman dh ga. Jadval