python-kerasHow can I use Python and Keras to build a deep learning model?

To use Python and Keras to build a deep learning model, the following steps should be followed:

1. Install the necessary packages, such as TensorFlow, Keras, and Scikit-Learn:

pip install tensorflow
pip install keras
pip install scikit-learn

2. Import the necessary packages:

import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense
import sklearn

3. Load the data:

# Load data
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()

4. Preprocess the data:

# Preprocess data
X_train = X_train.reshape(60000, 784).astype('float32')
X_test = X_test.reshape(10000, 784).astype('float32')
X_train /= 255
X_test /= 255

5. Create the model:

# Create model
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=784))
model.add(Dense(units=10, activation='softmax'))

6. Compile the model:

# Compile model
model.compile(loss='sparse_categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

7. Train the model:

# Train model
model.fit(X_train, y_train, epochs=5, batch_size=32)

After completing these steps, the model should be ready for evaluation and prediction.

Helpful links

• Keras Documentation
• TensorFlow Documentation
• Scikit-Learn Documentation

Edit this code on GitHub