deepthink 0.1.0

Deep Learning library

DeepThink

DeepThink is a deep learning library for Python, designed as a learning project and as a resource for others looking to learn about deep learning. It provides a high-level interface for building, training, and evaluating deep learning models, as well as a range of utilities for working with data and optimizing models.

Features

• A high-level API for defining, training, and evaluating models with minimal code
• Utilities for data loading, preprocessing, and model evaluation
• Tools for debugging, profiling, and optimizing models
• Extensive documentation and examples

Please note that DeepThink is a basic deep learning library and may not have the same level of performance or support for advanced features as other more established deep learning libraries.

Installation

pip install deepthink

Quickstart

Here is a simple example of how to use DeepThink to train a deep learning model:

from optimizers import Adam
from layers import Dense, Conv2D, MaxPooling, Flatten
from model import Model
from activations import ReLU,  Softmax
from utils import load_mnist_data
from loss import cce_cost_function

# Load dataset
training_data, test_data = load_mnist_data()

# Creating a model
optimizer = Adam(0.001)
model = Model(optimizer, cost=cce_cost_function, batch_size=64)

model.add_layer(Conv2D(kernel_size=5, n_filters=8,
                       input_shape=(64, 1, 28, 28)))
model.add_layer(ReLU())
model.add_layer(MaxPooling())
model.add_layer(Flatten())
model.add_layer(Dense(16))
model.add_layer(ReLU())
model.add_layer(Dense(10)
model.add_layer(Softmax())

model.initialize()

# Train the model
history = model.train(training_data, test_data, epochs=5)

For more information and examples, see the documentation.

Contributing

We welcome contributions to DeepThink! If you would like to report a bug, request a feature, or contribute code, please see our contributing guidelines.

License

DeepThink is released under the MIT License.