An open API service indexing awesome lists of open source software.

https://github.com/vvaldesc/keras_zalando_dataset


https://github.com/vvaldesc/keras_zalando_dataset

Last synced: about 1 month ago
JSON representation

Awesome Lists containing this project

README

        

# Keras Zalando Dataset

This repository contains a project using Keras to work with the Zalando Fashion MNIST dataset for image classification.

## Installation

1. Clone the repository:
```bash
git clone https://github.com/vvaldesc/Keras_Zalando_Dataset.git
```
2. Navigate to the project directory:
```bash
cd Keras_Zalando_Dataset
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```

## Usage

1. Load and preprocess the dataset:
```python
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
training_images = training_images / 255.0
test_images = test_images / 255.0
```

2. Build and compile the model:
```python
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
```

3. Train the model:
```python
model.fit(training_images, training_labels, epochs=10)
```

4. Evaluate the model:
```python
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('\nTest accuracy:', test_acc)
```

5. Make predictions:
```python
predictions = model.predict(test_images)
print(predictions[0])
```

## Contributors

- [vvaldesc](https://github.com/vvaldesc)
- [DaniGhr43](https://github.com/DaniGhr43)
- [PatriciaIA](https://github.com/pfernandezdi)

For more details on commits and project updates, visit the [commits page](https://github.com/vvaldesc/Keras_Zalando_Dataset/commits).

---

Please let me know if you need any additional sections or information included in the README.