Model a mlp using 1x1 cnn
Modeling an MLP Using 1x1 CNN for Regression in Deep Learning
Hey there! If you've been working with deep learning, you’ve probably come across Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). But did you know you can use Model a mlp using 1x1 cnn and make it more efficient? This approach is particularly useful for regression tasks in deep learning.

In this post, we’ll break it down step by step in a simple, easy-to-follow way. Whether you’re new to deep learning or just looking to improve your regression models, this guide will help you get started.
🔍 What is a Multilayer Perceptron (MLP)?
A Multilayer Perceptron (MLP) is one of the simplest types of neural networks. It consists of multiple layers of neurons where each neuron is connected to every neuron in the next layer.
A typical MLP for regression tasks follows this structure:
Input layer: Takes numerical features as input.
Hidden layers: Contain multiple neurons with activation functions to learn complex patterns.
Output layer: Produces a continuous numerical output (for regression).
🔹 Why Use MLPs for Regression?
Regression tasks involve predicting continuous values, such as:✅ Predicting house prices based on features like size, location, and age.✅ Estimating sales revenue from marketing spend.✅ Forecasting stock prices based on historical data.
MLPs are widely used for these tasks because they are simple, effective, and work well with structured data. But can we make them better using 1x1 CNNs? Absolutely!
🔍 What is a 1x1 Convolution?
A 1x1 convolution is a special type of convolutional filter where the kernel size is 1x1. Unlike traditional CNN filters that detect spatial patterns, 1x1 CNNs act like fully connected (dense) layers but with some key advantages:
✅ Fewer Parameters: 1x1 convolutions reduce the number of parameters compared to fully connected layers.✅ Faster Computation: Since they work as pointwise convolutions, they process data more efficiently.✅ Channel-Wise Computation: Instead of capturing spatial features, they work across feature maps.
Now, let’s see how we can use 1x1 CNNs to replace MLP layers in a deep learning regression model.
🛠️ Modeling an MLP Using 1x1 CNN for Regression
Instead of using dense layers in an MLP, we can replace them with 1x1 convolutional layers. Here’s how it works:
1️⃣ Instead of a fully connected layer, we use a 1x1 CNN to process the input.2️⃣ The activation functions remain the same (ReLU, Sigmoid, etc.).3️⃣ The final output layer remains a single neuron (for regression).
Let’s build a simple regression model using TensorFlow/Keras that replaces MLP layers with 1x1 convolutions.
📌 Step 1: Import Libraries
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, Flatten, Dense, Input
from tensorflow.keras.models import Model
import numpy as np
📌 Step 2: Prepare the Data
For this example, let's create some dummy regression data. Assume we are predicting house prices based on two features: square footage and number of rooms.
# Generate synthetic regression data
np.random.seed(42)
X_train = np.random.rand(1000, 1, 1, 2) # Input shape (batch, height, width, channels)
y_train = X_train[:, 0, 0, 0] 500000 + X_train[:, 0, 0, 1] 100000 # Simple linear equation
X_test = np.random.rand(200, 1, 1, 2)
y_test = X_test[:, 0, 0, 0] 500000 + X_test[:, 0, 0, 1] 100000
📌 Step 3: Build the Model Using 1x1 CNN
Here’s the interesting part! Instead of dense layers, we use 1x1 CNNs for feature transformation.
# Define the model
input_layer = Input(shape=(1, 1, 2)) # Input shape matches the data
# First 1x1 Convolution Layer (acts like Dense)
conv1 = Conv2D(16, kernel_size=(1, 1), activation='relu')(input_layer)
# Second 1x1 Convolution Layer
conv2 = Conv2D(8, kernel_size=(1, 1), activation='relu')(conv1)
# Flatten the output
flatten = Flatten()(conv2)
# Fully Connected Output Layer for Regression
output_layer = Dense(1, activation='linear')(flatten)
# Create the model
model = Model(inputs=input_layer, outputs=output_layer)
📌 Step 4: Compile and Train the Model
Now, let’s compile the model with the Mean Squared Error (MSE) loss function and train it.
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=16, validation_data=(X_test, y_test))
📌 Step 5: Make Predictions
After training, we can make predictions on new data.
y_pred = model.predict(X_test)
# Print first 5 predictions vs actual values
for i in range(5):
print(f"Predicted: {y_pred[i][0]:.2f}, Actual: {y_test[i]:.2f}")
🎯 Why Use 1x1 CNN Instead of Dense Layers?
You might be wondering—why not just use fully connected layers? Well, here’s why 1x1 CNNs are useful:
✅ Parameter Efficiency: 1x1 CNNs reduce the number of trainable parameters, making the model lighter and faster.✅ Spatial Flexibility: They can work with multi-dimensional feature maps, making them great for handling structured and image-like data.✅ Easier Regularization: Since CNNs naturally support batch normalization and dropout, it’s easier to prevent overfitting.
🏁 Final Thoughts
Using 1x1 CNNs to model MLPs for regression is a clever way to reduce parameters, improve efficiency, and maintain accuracy. While traditional fully connected layers work fine, 1x1 convolutions offer better scalability and flexibility, especially for structured data.
So, the next time you're building a deep learning regression model, give 1x1 CNNs a try—you might be surprised at how well they perform!
Got any questions or suggestions? Drop a comment below, or check out my other deep learning tutorials at blog.arunangshudas.com. Let’s learn and grow together! 🚀