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.

• 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
• A range of examples to illustrate several use cases

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.

pip install deepthink

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

from deepthink.optimizers import Adam
from deepthink.layers import Dense, Conv2D, MaxPooling2D, Flatten
from deepthink.model import Model
from deepthink.activations import ReLU,  Softmax
from deepthink.utils import load_mnist_data
from deepthink.loss import CategoricalCrossEntropy

# Load dataset
training_data, test_data = load_mnist_data()

# Creating a model
optimizer = Adam(0.001)
model = Model(optimizer, cost=CategoricalCrossEntropy(), 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(MaxPooling2D())
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)

You can find additional examples in the examples directory.

Contributions are more than welcome to DeepThink! If you would like to report a bug, request a feature, or contribute code, please create an issue or submit a pull request.

DeepThink is released under the MIT License.