Integration and ODE solvers for TensorFlow.
We can use odeint
to solve the Lorentz system of ordinary differential equations, a prototypical example of chaotic dynamics:
rho = 28.0 sigma = 10.0 beta = 8.0/3.0 def lorenz_equation(state, t): x, y, z = tf.unstack(state) dx = sigma * (y - x) dy = x * (rho - z) - y dz = x * y - beta * z return tf.stack([dx, dy, dz]) init_state = tf.constant([0, 2, 20], dtype=tf.float64) t = np.linspace(0, 50, num=5000) tensor_state, tensor_info = tf.contrib.integrate.odeint( lorenz_equation, init_state, t, full_output=True) sess = tf.Session() state, info = sess.run([tensor_state, tensor_info]) x, y, z = state.T plt.plot(x, z)
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Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_guides/python/contrib.integrate