Python tensorflow.python.ops.gen_nn_ops._batch_norm_with_global_normalization_grad() Examples
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Example #1
Source File: nn_grad.py From lambda-packs with MIT License | 6 votes |
def _BatchNormWithGlobalNormalizationGrad(op, grad): """Return the gradients for the 5 inputs of BatchNormWithGlobalNormalization. We do not backprop anything for the mean and var intentionally as they are not being trained with backprop in the operation. Args: op: The BatchNormOp for which we need to generate gradients. grad: Tensor. The gradients passed to the BatchNormOp. Returns: dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon))) dm: Backprop for mean, which is sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon)) dv: Backprop for variance, which is sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2) db: Backprop for beta, which is grad reduced in all except the last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg
Example #2
Source File: nn_grad.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def _BatchNormWithGlobalNormalizationGrad(op, grad): """Return the gradients for the 5 inputs of BatchNormWithGlobalNormalization. We do not backprop anything for the mean and var intentionally as they are not being trained with backprop in the operation. Args: op: The BatchNormOp for which we need to generate gradients. grad: Tensor. The gradients passed to the BatchNormOp. Returns: dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon))) dm: Backprop for mean, which is sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon)) dv: Backprop for variance, which is sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2) db: Backprop for beta, which is grad reduced in all except the last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg
Example #3
Source File: nn_grad.py From deep_image_model with Apache License 2.0 | 6 votes |
def _BatchNormWithGlobalNormalizationGrad(op, grad): """Return the gradients for the 5 inputs of BatchNormWithGlobalNormalization. We do not backprop anything for the mean and var intentionally as they are not being trained with backprop in the operation. Args: op: The BatchNormOp for which we need to generate gradients. grad: Tensor. The gradients passed to the BatchNormOp. Returns: dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon))) dm: Backprop for mean, which is sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon)) dv: Backprop for variance, which is sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2) db: Backprop for beta, which is grad reduced in all except the last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg
Example #4
Source File: nn_grad.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def _BatchNormWithGlobalNormalizationGrad(op, grad): """Return the gradients for the 5 inputs of BatchNormWithGlobalNormalization. We do not backprop anything for the mean and var intentionally as they are not being trained with backprop in the operation. Args: op: The BatchNormOp for which we need to generate gradients. grad: Tensor. The gradients passed to the BatchNormOp. Returns: dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon))) dm: Backprop for mean, which is sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon)) dv: Backprop for variance, which is sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2) db: Backprop for beta, which is grad reduced in all except the last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg
Example #5
Source File: nn_grad.py From keras-lambda with MIT License | 6 votes |
def _BatchNormWithGlobalNormalizationGrad(op, grad): """Return the gradients for the 5 inputs of BatchNormWithGlobalNormalization. We do not backprop anything for the mean and var intentionally as they are not being trained with backprop in the operation. Args: op: The BatchNormOp for which we need to generate gradients. grad: Tensor. The gradients passed to the BatchNormOp. Returns: dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon))) dm: Backprop for mean, which is sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon)) dv: Backprop for variance, which is sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2) db: Backprop for beta, which is grad reduced in all except the last dimension. dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon))) """ dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad, op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization")) return dx, dm, dv, db, dg
Example #6
Source File: nn_batchnorm_test.py From deep_image_model with Apache License 2.0 | 4 votes |
def testBatchNormGradImpl(self): x_shape = [7, 5, 4, 6] param_shape = [6] np.random.seed(1) # Make it reproducible. x_val = np.random.random_sample(x_shape).astype(np.float32) m_val = np.random.random_sample(param_shape).astype(np.float32) v_val = np.random.random_sample(param_shape).astype(np.float32) beta_val = np.random.random_sample(param_shape).astype(np.float32) gamma_val = np.random.random_sample(param_shape).astype(np.float32) backprop_val = np.random.random_sample(x_shape).astype(np.float32) for use_gpu in [False, True]: with self.test_session(use_gpu=use_gpu) as sess: x = tf.constant(x_val, name="x") m = tf.constant(m_val, name="m") v = tf.constant(v_val, name="v") beta = tf.constant(beta_val, name="beta") gamma = tf.constant(gamma_val, name="gamma") backprop = tf.constant(backprop_val, name="backprop") epsilon = 0.001 for scale_after_normalization in [True, False]: # _batch_norm_with_global_normalization_grad is deprecated in v9 tf.get_default_graph().graph_def_versions.producer = 8 grad = gen_nn_ops._batch_norm_with_global_normalization_grad( x, m, v, gamma, backprop, epsilon, scale_after_normalization) dx, dm, dv, db, dg = grad self.assertEqual(grad.dx, dx) self.assertEqual(grad.dm, dm) self.assertEqual(grad.dv, dv) self.assertEqual(grad.db, db) self.assertEqual(grad.dg, dg) on = self._opsBatchNorm( x, m, v, beta, gamma, epsilon, scale_after_normalization, True) odx, odm, odv, odb, odg = tf.gradients( [on], [x, m, v, beta, gamma], [backprop]) if scale_after_normalization: all_grads = sess.run([dx, dm, dv, db, dg, odx, odm, odv, odb, odg]) to_check = ["dx", "dm", "dv", "db", "dg"] else: all_grads = sess.run([dx, dm, dv, db, odx, odm, odv, odb]) to_check = ["dx", "dm", "dv", "db"] for i, _ in enumerate(to_check): self.assertAllClose( all_grads[i + len(to_check)], all_grads[i], atol=0.000001)