Python tensorflow.neg() Examples

The following are 14 code examples of tensorflow.neg(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source File: subscribe_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up
def testSideEffect(self):
    a = tf.constant(1)
    b = tf.constant(1)
    c = tf.add(a, b)
    with tf.control_dependencies([c]):
      d = tf.constant(42)
    n = tf.neg(c)

    shared = []

    def sub(t):
      shared.append(t)
      return t

    c = subscribe.subscribe(c, lambda t: tf.py_func(sub, [t], [t.dtype]))

    with self.test_session() as sess:
      c_out = sess.run([c])
      n_out = sess.run([n])
      d_out = sess.run([d])

    self.assertEquals(n_out, [-2])
    self.assertEquals(c_out, [2])
    self.assertEquals(d_out, [42])
    self.assertEquals(shared, [2, 2, 2]) 
Example #2
Source File: ddpg_cartpole.py    From cartpoleplusplus with MIT License 6 votes vote down vote up
def init_ops_for_training(self, critic):
    # actors gradients are the gradients for it's output w.r.t it's vars using initial
    # gradients provided by critic. this requires that critic was init'd with an
    # input_action = actor.output_action (which is natural anyway)
    # we wrap the optimiser in namespace since we don't want this as part of copy to
    # target networks.
    # note that we negate the gradients from critic since we are trying to maximise
    # the q values (not minimise like a loss)
    with tf.variable_scope("optimiser"):
      gradients = tf.gradients(self.output_action,
                               self.trainable_model_vars(),
                               tf.neg(critic.q_gradients_wrt_actions()))
      gradients = zip(gradients, self.trainable_model_vars())
      # potentially clip and wrap with debugging
      gradients = util.clip_and_debug_gradients(gradients, opts)
      # apply
      optimiser = tf.train.GradientDescentOptimizer(opts.actor_learning_rate)
      self.train_op = optimiser.apply_gradients(gradients) 
Example #3
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testFloatBasic(self):
    x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32)
    y = (x + .5).astype(np.float32)     # no zero
    z = (x + 15.5).astype(np.float32)   # all positive
    k = np.arange(-0.90, 0.90, 0.25).astype(np.float32) # between -1 and 1

    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(y, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(z, np.sqrt, tf.sqrt)
    self._compareBoth(z, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(z, np.log, tf.log)
    self._compareBoth(z, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(y, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    self._compareBoth(k, np.arcsin, tf.asin)
    self._compareBoth(k, np.arccos, tf.acos)
    self._compareBoth(x, np.arctan, tf.atan)
    self._compareBoth(x, np.tan, tf.tan)
    self._compareBoth(
        y,
        np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
        tf.lgamma)
    self._compareBoth(x, np.vectorize(math.erf), tf.erf)
    self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(y, np.sign, tf.sign)
    self._compareBothSparse(x, np.vectorize(math.erf), tf.erf) 
Example #4
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testFloatEmpty(self):
    x = np.empty((2, 0, 5), dtype=np.float32)
    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(x, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(x, np.sqrt, tf.sqrt)
    self._compareBoth(x, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(x, np.log, tf.log)
    self._compareBoth(x, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(x, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    # Can't use vectorize below, so just use some arbitrary function
    self._compareBoth(x, np.sign, tf.lgamma)
    self._compareBoth(x, np.sign, tf.erf)
    self._compareBoth(x, np.sign, tf.erfc)
    self._compareBoth(x, np.tan, tf.tan)
    self._compareBoth(x, np.arcsin, tf.asin)
    self._compareBoth(x, np.arccos, tf.acos)
    self._compareBoth(x, np.arctan, tf.atan)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(x, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(x, np.sign, tf.sign)
    self._compareBothSparse(x, np.sign, tf.erf) 
Example #5
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testDoubleBasic(self):
    x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64)
    y = (x + .5).astype(np.float64)    # no zero
    z = (x + 15.5).astype(np.float64)  # all positive
    k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(np.float64) # between -1 and 1
    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(y, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(z, np.sqrt, tf.sqrt)
    self._compareBoth(z, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(z, np.log, tf.log)
    self._compareBoth(z, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(y, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    self._compareBoth(
        y,
        np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
        tf.lgamma)
    self._compareBoth(x, np.vectorize(math.erf), tf.erf)
    self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
    self._compareBoth(x, np.arctan, tf.atan)
    self._compareBoth(k, np.arcsin, tf.asin)
    self._compareBoth(k, np.arccos, tf.acos)
    self._compareBoth(k, np.tan, tf.tan)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(y, np.sign, tf.sign)
    self._compareBothSparse(x, np.vectorize(math.erf), tf.erf) 
Example #6
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testHalfBasic(self):
    x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float16)
    y = (x + .5).astype(np.float16)    # no zero
    z = (x + 15.5).astype(np.float16)  # all positive
    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(y, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(z, np.sqrt, tf.sqrt)
    self._compareBoth(z, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(z, np.log, tf.log)
    self._compareBoth(z, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(y, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    self._compareBoth(
        y,
        np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
        tf.lgamma)
    self._compareBoth(x, np.vectorize(math.erf), tf.erf)
    self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(y, np.sign, tf.sign)
    self._compareBothSparse(x, np.vectorize(math.erf), tf.erf, tol=1e-3) 
Example #7
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testInt32Basic(self):
    x = np.arange(-6, 6, 2).reshape(1, 3, 2).astype(np.int32)
    self._compareCpu(x, np.abs, tf.abs)
    self._compareCpu(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(x, np.square, tf.square)
    self._compareCpu(x, np.sign, tf.sign)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(x, np.sign, tf.sign) 
Example #8
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testComplex64Basic(self):
    x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype(
        np.complex64)
    y = x + 0.5  # no zeros
    self._compareCpu(x, np.abs, tf.complex_abs)
    self._compareCpu(x, np.abs, _ABS)
    self._compareCpu(x, np.negative, tf.neg)
    self._compareCpu(x, np.negative, _NEG)
    self._compareCpu(y, self._inv, tf.inv)
    self._compareCpu(x, np.square, tf.square)
    self._compareCpu(y, np.sqrt, tf.sqrt)
    self._compareCpu(y, self._rsqrt, tf.rsqrt)
    self._compareCpu(x, np.exp, tf.exp)
    self._compareCpu(y, np.log, tf.log)
    self._compareCpu(y, np.log1p, tf.log1p)
    self._compareCpu(x, np.tanh, tf.tanh)
    self._compareCpu(x, self._sigmoid, tf.sigmoid)
    self._compareCpu(x, np.sin, tf.sin)
    self._compareCpu(x, np.cos, tf.cos)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(x, np.sqrt, tf.sqrt, 1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)

    # Numpy uses an incorrect definition of sign; use the right one instead.
    def complex_sign(x):
      return x / np.abs(x)
    self._compareCpu(y, complex_sign, tf.sign)
    self._compareBothSparse(y, complex_sign, tf.sign) 
Example #9
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up
def testComplex128Basic(self):
    x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype(
        np.complex128)
    y = x + 0.5  # no zeros
    self._compareCpu(x, np.abs, tf.abs)
    self._compareCpu(x, np.abs, _ABS)
    self._compareCpu(x, np.negative, tf.neg)
    self._compareCpu(x, np.negative, _NEG)
    self._compareCpu(y, self._inv, tf.inv)
    self._compareCpu(x, np.square, tf.square)
    self._compareCpu(y, np.sqrt, tf.sqrt)
    self._compareCpu(y, self._rsqrt, tf.rsqrt)
    self._compareCpu(x, np.exp, tf.exp)
    self._compareCpu(y, np.log, tf.log)
    self._compareCpu(y, np.log1p, tf.log1p)
    self._compareCpu(x, np.tanh, tf.tanh)
    self._compareCpu(x, self._sigmoid, tf.sigmoid)
    self._compareCpu(x, np.sin, tf.sin)
    self._compareCpu(x, np.cos, tf.cos)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(x, np.sqrt, tf.sqrt, 1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)

    # Numpy uses an incorrect definition of sign; use the right one instead.
    def complex_sign(x):
      return x / np.abs(x)
    self._compareCpu(y, complex_sign, tf.sign)
    self._compareBothSparse(y, complex_sign, tf.sign) 
Example #10
Source File: model.py    From tensorflow_tmva with GNU General Public License v2.0 5 votes vote down vote up
def activation(type, synapse):
    """Chooses the activation function to use."""
    if type == "sigmoid":
        return tf.sigmoid(synapse)
    elif type == "linear":
        return synapse
    elif type == "tanh":
        return tf.tanh(synapse)
    elif type == "radial":
        return tf.sqrt(tf.exp(tf.neg(tf.square(synapse)))) 
Example #11
Source File: svm.py    From tensorflow_tmva with GNU General Public License v2.0 5 votes vote down vote up
def gaussian_kernel(tensor_a, a_inputs, tensor_b, b_inputs, gamma):
    """Returns the Gaussian kernel matrix of two matrices of vectors
    element-wise."""
    cross = cross_matrices(tensor_a, a_inputs, tensor_b, b_inputs)

    kernel = tf.exp(tf.mul(tf.reduce_sum(tf.square(
        tf.sub(cross[0], cross[1])), reduction_indices=2),
        tf.neg(tf.constant(gamma, dtype=tf.float32))))

    return kernel 
Example #12
Source File: tf_distributions.py    From MJHMC with GNU General Public License v2.0 5 votes vote down vote up
def build_energy_op(self):
        with self.graph.as_default(), tf.device(self.energy_device):
            # [1, nbatch]
            e_x_0 = tf.neg((self.state_pl[0, :] ** 2) / (self.scale ** 2), name='E_x_0')
            # [ndims - 1, nbatch]
            e_x_k = tf.neg((self.state_pl[1:, :] ** 2) / tf.exp(self.state_pl[0, :]), name='E_x_k')
            # [nbatch]
            self.energy_op = tf.reduce_sum(tf.add(e_x_0, e_x_k), 0, name='energy_op') 
Example #13
Source File: flip_gradient.py    From active_learning_coreset with MIT License 5 votes vote down vote up
def __call__(self, x, l=1.0):
        grad_name = "FlipGradient%d" % self.num_calls
        @ops.RegisterGradient(grad_name)
        def _flip_gradients(op, grad):
            return [tf.neg(grad) * l]
        
        g = tf.get_default_graph()
        with g.gradient_override_map({"Identity": grad_name}):
            y = tf.identity(x)
            
        self.num_calls += 1
        return y 
Example #14
Source File: triplet_clustering.py    From active_learning_coreset with MIT License 5 votes vote down vote up
def loss_function(self):
        pos_diff = self.anchor - self.positive
        neg_diff = self.anchor - self.negative

        pos_dist = tf.reduce_sum(tf.mul(pos_diff, pos_diff), 1)
        neg_dist = tf.reduce_sum(tf.mul(neg_diff, neg_diff), 1)

        triplet = tf.add(self.ALPHA, tf.add(pos_dist, tf.neg(neg_dist)))
        return tf.reduce_sum(tf.nn.relu(triplet))