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Python tensorflow.half方法代码示例

本文整理汇总了Python中tensorflow.half方法的典型用法代码示例。如果您正苦于以下问题:Python tensorflow.half方法的具体用法?Python tensorflow.half怎么用?Python tensorflow.half使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow的用法示例。


在下文中一共展示了tensorflow.half方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: testBasic

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testBasic(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(zip(
            [grads0, grads1], [var0, var1]))
        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            [1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
        self.assertAllCloseAccordingToType(
            [3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:22,代码来源:gradient_descent_test.py

示例2: testTensorLearningRate

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testTensorLearningRate(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        lrate = tf.constant(3.0)
        sgd_op = tf.train.GradientDescentOptimizer(lrate).apply_gradients(zip(
            [grads0, grads1], [var0, var1]))
        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            [1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
        self.assertAllCloseAccordingToType(
            [3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:23,代码来源:gradient_descent_test.py

示例3: testWithGlobalStep

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testWithGlobalStep(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        global_step = tf.Variable(0, trainable=False)
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(
            zip([grads0, grads1], [var0, var1]),
            global_step=global_step)
        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([3.0, 4.0], var1.eval())
        # Run 1 step of sgd
        sgd_op.run()
        # Validate updated params and global_step
        self.assertAllCloseAccordingToType(
            [1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
        self.assertAllCloseAccordingToType(
            [3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())
        self.assertAllCloseAccordingToType(1, global_step.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:25,代码来源:gradient_descent_test.py

示例4: testBasic

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testBasic(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        opt_op = sgd_op.minimize(cost, global_step, [var0, var1])

        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose([-14., -13.], var0.eval())
        self.assertAllClose([-6., -5.], var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:21,代码来源:optimizer_test.py

示例5: testAggregationMethod

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testAggregationMethod(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        opt_op = sgd_op.minimize(
            cost,
            global_step,
            [var0, var1],
            aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)

        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose([-14., -13.], var0.eval())
        self.assertAllClose([-6., -5.], var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:25,代码来源:optimizer_test.py

示例6: testPrecomputedGradient

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testPrecomputedGradient(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        cost = 5 * var0 + 3 * var1
        grad_loss = tf.constant([42, -42], dtype=dtype)
        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
        sgd_op = tf.train.GradientDescentOptimizer(3.0)
        opt_op = sgd_op.minimize(cost,
                                 global_step, [var0, var1],
                                 grad_loss=grad_loss)

        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 1 step of sgd through optimizer
        opt_op.run()
        # Validate updated params
        self.assertAllClose(
            [1.0 - 3 * 5 * 42.0, 2.0 - 3 * 5 * (-42.0)], var0.eval())
        self.assertAllClose(
            [3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:26,代码来源:optimizer_test.py

示例7: doTestBasic

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def doTestBasic(self, use_locking=False):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        ada_opt = tf.train.AdagradOptimizer(3.0,
                                            initial_accumulator_value=0.1,
                                            use_locking=use_locking)
        ada_update = ada_opt.apply_gradients(zip(
            [grads0, grads1], [var0, var1]))
        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 3 steps of adagrad
        for _ in range(3):
          ada_update.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval())
        self.assertAllCloseAccordingToType(
            np.array([2.715679168701172, 3.715679168701172]), var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:26,代码来源:adagrad_test.py

示例8: testTensorLearningRate

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testTensorLearningRate(self):
    for dtype in [tf.half, tf.float32, tf.float64]:
      with self.test_session():
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
        ada_opt = tf.train.AdagradOptimizer(
            tf.constant(3.0),
            initial_accumulator_value=0.1)
        ada_update = ada_opt.apply_gradients(zip(
            [grads0, grads1], [var0, var1]))
        tf.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([3.0, 4.0], var1.eval())
        # Run 3 steps of adagrad
        for _ in range(3):
          ada_update.run()
        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-1.6026098728179932, -0.6026098728179932]), var0.eval())
        self.assertAllCloseAccordingToType(
            np.array([2.715679168701172, 3.715679168701172]), var1.eval()) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:26,代码来源:adagrad_test.py

示例9: testEquivAdagradwithoutRegularization

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testEquivAdagradwithoutRegularization(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        val0, val1 = self.applyOptimizer(
            tf.train.FtrlOptimizer(3.0,
                                   # Adagrad learning rate
                                   learning_rate_power=-0.5,
                                   initial_accumulator_value=0.1,
                                   l1_regularization_strength=0.0,
                                   l2_regularization_strength=0.0),
            dtype)

      with self.test_session():
        val2, val3 = self.applyOptimizer(
            tf.train.AdagradOptimizer(3.0, initial_accumulator_value=0.1),
            dtype)

      self.assertAllCloseAccordingToType(val0, val2)
      self.assertAllCloseAccordingToType(val1, val3) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:21,代码来源:ftrl_test.py

示例10: testEquivSparseAdagradwithoutRegularization

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testEquivSparseAdagradwithoutRegularization(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        val0, val1 = self.applyOptimizer(
            tf.train.FtrlOptimizer(3.0,
                                   # Adagrad learning rate
                                   learning_rate_power=-0.5,
                                   initial_accumulator_value=0.1,
                                   l1_regularization_strength=0.0,
                                   l2_regularization_strength=0.0),
            dtype,
            is_sparse=True)

      with self.test_session():
        val2, val3 = self.applyOptimizer(
            tf.train.AdagradOptimizer(3.0, initial_accumulator_value=0.1),
            dtype, is_sparse=True)

      self.assertAllCloseAccordingToType(val0, val2)
      self.assertAllCloseAccordingToType(val1, val3) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:22,代码来源:ftrl_test.py

示例11: testEquivSparseGradientDescentwithoutRegularization

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testEquivSparseGradientDescentwithoutRegularization(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        val0, val1 = self.applyOptimizer(
            tf.train.FtrlOptimizer(3.0,
                                   # Fixed learning rate
                                   learning_rate_power=-0.0,
                                   initial_accumulator_value=0.1,
                                   l1_regularization_strength=0.0,
                                   l2_regularization_strength=0.0),
            dtype,
            is_sparse=True)

      with self.test_session():
        val2, val3 = self.applyOptimizer(
            tf.train.GradientDescentOptimizer(3.0), dtype, is_sparse=True)

      self.assertAllCloseAccordingToType(val0, val2)
      self.assertAllCloseAccordingToType(val1, val3) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:21,代码来源:ftrl_test.py

示例12: testEquivGradientDescentwithoutRegularization

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def testEquivGradientDescentwithoutRegularization(self):
    for dtype in [tf.half, tf.float32]:
      with self.test_session():
        val0, val1 = self.applyOptimizer(
            tf.train.FtrlOptimizer(3.0,
                                   # Fixed learning rate
                                   learning_rate_power=-0.0,
                                   initial_accumulator_value=0.1,
                                   l1_regularization_strength=0.0,
                                   l2_regularization_strength=0.0),
            dtype)

      with self.test_session():
        val2, val3 = self.applyOptimizer(
            tf.train.GradientDescentOptimizer(3.0), dtype)

      self.assertAllCloseAccordingToType(val0, val2)
      self.assertAllCloseAccordingToType(val1, val3) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:20,代码来源:ftrl_test.py

示例13: args_check

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def args_check(cls, node, **kwargs):
    supported_dtype = [
        tf.bfloat16, tf.half, tf.float32, tf.float64, tf.uint8, tf.uint16,
        tf.int8, tf.int16, tf.int32, tf.int64, tf.complex64, tf.complex128
    ]
    x = kwargs["tensor_dict"][node.inputs[0]]
    if x.dtype not in supported_dtype:
      exception.OP_UNSUPPORTED_EXCEPT(
          "CumSum input in " + str(x.dtype) + " which", "Tensorflow") 
开发者ID:onnx,项目名称:onnx-tensorflow,代码行数:11,代码来源:cumsum.py

示例14: args_check

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def args_check(cls, node, **kwargs):
    supported_dtype = [
        tf.bfloat16, tf.half, tf.float32, tf.float64, tf.uint8, tf.int8,
        tf.int16, tf.int32, tf.int64, tf.complex64, tf.quint8, tf.qint8,
        tf.qint32, tf.string, tf.bool, tf.complex128
    ]
    x = kwargs["tensor_dict"][node.inputs[0]]
    if x.dtype not in supported_dtype:
      exception.OP_UNSUPPORTED_EXCEPT(
          "Equal inputs in " + str(x.dtype) + " which", "Tensorflow") 
开发者ID:onnx,项目名称:onnx-tensorflow,代码行数:12,代码来源:equal.py

示例15: _dtypes_to_test

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import half [as 别名]
def _dtypes_to_test(use_gpu):
    # Based on issue #347 (https://github.com/tensorflow/addons/issues/347)
    # tf.half is not registered for 'ResourceScatterUpdate' OpKernel for 'GPU'.
    # So we have to remove tf.half when testing with gpu.
    if use_gpu:
        return [tf.float32, tf.float64]
    else:
        return [tf.half, tf.float32, tf.float64] 
开发者ID:tensorflow,项目名称:addons,代码行数:10,代码来源:lamb_test.py


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