How to keep track of Keras Models with CodeMonitor?

Introduction

In today's fast−paced world, machine learning models developed using frameworks like Keras have transformed various industries. However, keeping track of these models and their iterations can become a challenging task for data scientists and developers alike. CodeMonitor is an innovative tool that simplifies model versioning, monitoring, and collaboration for seamless experimentation and development workflows. In this article, we will dive into how CodeMonitor effortlessly enhances the management of Keras models through a practical example.

Keras Models with CodeMonitor

• Version Control: With each training session or modification performed on the model saved as a separate commit or pull request in Git (or any other supported repository), you can effortlessly navigate between past versions using clear labels and descriptions provided through CodeMonitor's user−friendly interface.

• Documentation Workflow: By connecting directly with Jupyter Notebooks associated with your project via API calls or simple configuration setup in minutes, you can share detailed explanations alongside relevant implementations within notebooks themselves! This greatly enhances reproducibility and comprehension across team members involved in collaborative ML projects.

• Experiment Visualization: View all key metrics (e.g., accuracy curves over time), confident predictions made during testing phases right inside CodeMonitor's robust dashboard, and compare different model iterations to identify patterns or insights leading to further improvements.

Streamlined Collaboration

CodeMonitor facilitates streamlined collaboration across teams:

• Access Control: Manage user permissions efficiently for added privacy and security, ensuring that each team member has appropriate access levels according to their roles.

• Real−time Notifications: Stay updated with real−time notifications for every code change, commit or additional comment made by other contributors. This enhances transparency and enables prompt engagement when required.

• Model Sharing: Easily share models with collaborators via unique URLs generated on CodeMonitor, which grant read−only access without divulging your underlying code logic. Share your progress with external stakeholders for feedback or validation purposes effortlessly.

Integration−Friendly Environment

CodeMonitor seamlessly integrates with various platforms enabling a seamless workflow:

• Reproducible Environments: Run models in the same environment regardless of where they were developed initially − be it cloud−based setups like AWS Lambda functions, locally−hosted environments using Docker containers, or within frameworks like TensorFlow Serving instances!

• Popular ML Frameworks Support: While Keras is the prime focus of CodeMonitor due to its ease−of−use and versatility among data scientists today, it can also handle complex workflows involving other popular deep learning frameworks such as PyTorch or Tensorflow through unified tracking mechanisms.

Python Code to Keep Track of Keras Models with CodeMonitor

Let's imagine we are working on an image classification project where our goal is to design a deep neural network using Keras to classify images into different categories.

Algorithm

Step 1 :Import the Keras library.

Step 2 :Import the Sequential model, Conv2D, MaxPooling2D, Flatten, and Dense layers from Keras.

Step 3 :Import the CodeMonitor library and initialize a CodeMonitor session.

Step 4 :Define our neural network architecture as per our requirements.

Step 5 :Create a new Sequential model.

Step 6 :Add a max pooling layer by invoking MaxPooling2D() function.

Step 7 :Add a flatten layer to the model and a dense layer with 128 units and ReLU activation to the model.

Step 8 :Add a dense layer with 1 unit and sigmoid activation to the model.

Step 9 :Utilize the suitable loss function, optimizer, and metrics to compile the model.

Step 10 :Train our model using relevant training dataFinalize CodeMonitor session after model training completes.

Example

import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
import codemonitor

# Initialize CodeMonitor session
codemonitor.start()

# Define your neural network architecture as per your requirements:
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64 ,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2 , 2)))
model.add(Flatten())
model.add(Dense(units = 128, activation ='relu'))
model.add(Dense(units = 1 , activation ='sigmoid'))

# Compile the model with suitable loss function, optimizer, and metrics:
model.compile(optimizer='adam', loss='binary_crossentropy',
              metrics=['accuracy'])

# Train your model using relevant training data:
model.fit(x_train, y_train, epochs=10)

# Finalize CodeMonitor session after model training completes.
codemonitor.end()

Output

Epoch 1/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6932 - accuracy: 0.5000
Epoch 2/10
100/100 [==============================] - 0s 5ms/step - loss: 0.6929 - accuracy: 0.5000
Epoch 3/10
100/100 [==============================] - 0s 5ms/step - loss: 0.6927 - accuracy: 0.5000
Epoch 4/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6924 - accuracy: 0.5000
Epoch 5/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6922 - accuracy: 0.5000
Epoch 6/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6919 - accuracy: 0.5000
Epoch 7/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6917 - accuracy: 0.5000
Epoch 8/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6914 - accuracy: 0.5000
Epoch 9/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6912 - accuracy: 0.5000
Epoch 10/10
100/100 [==============================] - 1s 5ms/step - loss: 0.6909 - accuracy: 0.5000

Conclusion

Keeping track of Keras models becomes effortless with CodeMonitor's powerful features designed specifically for managing machine learning projects effectively. By integrating repository linkage features alongside version control capabilities, documentation workflows tailored towards Jupyter Notebooks integration and collaborative accessibility paired up with experiment visualization options − CodeMonitor truly transforms how AI teams operate together while keeping project−related assets organized.