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Build models by stacking layers, optimizers, and loss functions in a clean, readable format.
Quickly test and iterate on ideas with minimal code and intuitive APIs.
Run Keras on TensorFlow, JAX, or Theano backends for flexible deployment.
Designed for simplicity and clarity—perfect for students, educators, and newcomers to deep learning.
Use pip to install Keras as part of TensorFlow or standalone via `pip install keras`.
Load Keras layers, models, and utilities to start building your neural network.
Use Sequential or Functional API to define architecture with layers like Dense, Conv2D, LSTM.
Choose optimizer, loss function, and metrics. Train with `.fit()` and validate with `.evaluate()`.
Save models using `.save()` and deploy via TensorFlow Serving, Lite, or JS.
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# Define a simple model
model = Sequential([
Dense(64, activation='relu', input_shape=(10,)),
Dense(1)
])
# Compile the model
model.compile(optimizer='adam', loss='mse')
# Dummy data
x_train = np.random.rand(100, 10)
y_train = np.random.rand(100, 1)
# Train the model
model.fit(x_train, y_train, epochs=5)
# Evaluate
loss = model.evaluate(x_train, y_train)
print("Loss:", loss)
# Save the model
model.save("my_model.keras")Perfect for teaching neural networks and ML concepts in classrooms and workshops.
Build CNNs using Keras layers to classify images with minimal code.
Use embedding layers and LSTMs for sentiment analysis, spam detection, and more.
Predict continuous values like prices, scores, or trends using dense networks.
Explore Keras’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Keras’s capabilities, usage, and ecosystem.