cplx_gradient_descent_test.py 文件源码

def testTensorLearningRate(self):
    for dtype in [tf.complex64]:
      with self.test_session(force_gpu=True):
        v0 = [1.0+2.0j, 2.0+1.0j]
        v1 = [3.0-4.0j, 4.0-3.0j]
        g0 = [0.1+0.1j, 0.1-0.1j]
        g1 = [0.01-0.01j, 0.01+0.01j]
        lr = 3.0-1.5j 
        var0 = tf.Variable(v0, dtype=dtype)
        var1 = tf.Variable(v1, dtype=dtype)
        grads0 = tf.constant(g0, dtype=dtype)
        grads1 = tf.constant(g1, dtype=dtype)
        lrate = tf.constant(lr)
        sgd_op = ctf.train.CplxGradientDescentOptimizer(
          lrate).apply_gradients(zip([grads0, grads1], [var0, var1]))
        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType(v0, var0.eval())
        self.assertAllCloseAccordingToType(v1, var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            [v0[0] - lr * g0[0],
             v0[1] - lr * g0[1]], var0.eval())
        self.assertAllCloseAccordingToType(
            [v1[0] - lr * g1[0],
             v1[1] - lr * g1[1]], var1.eval())