Python tensorflow.python.keras.backend.mean() Examples
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code examples of tensorflow.python.keras.backend.mean().
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Example #1
Source File: normalization.py From icme2019 with MIT License | 5 votes |
def call(self, x): mean = K.mean(x, axis=self.axis, keepdims=True) std = K.std(x, axis=self.axis, keepdims=True) return self.gamma * (x - mean) / (std + self.eps) + self.beta
Example #2
Source File: sdne.py From GraphEmbedding with MIT License | 5 votes |
def l_2nd(beta): def loss_2nd(y_true, y_pred): b_ = np.ones_like(y_true) b_[y_true != 0] = beta x = K.square((y_true - y_pred) * b_) t = K.sum(x, axis=-1, ) return K.mean(t) return loss_2nd
Example #3
Source File: line.py From GraphEmbedding with MIT License | 5 votes |
def line_loss(y_true, y_pred): return -K.mean(K.log(K.sigmoid(y_true*y_pred)))
Example #4
Source File: backend.py From FATE with Apache License 2.0 | 5 votes |
def call(self, x, mask=None): """1, mask is a bool type tensor, need casting before compute. 2, mask shape in 2 dimension (batch_size, feature_dimension) """ if mask is not None: mask = K.repeat(mask, x.shape[-1]) mask = tf.transpose(mask, [0, 2, 1]) mask = tf.cast(mask, tf.float32) x = x * mask return K.sum(x, axis=1) / K.sum(mask, axis=1) else: return K.mean(x, axis=1)
Example #5
Source File: sample_layer.py From BVAE-tf with The Unlicense | 5 votes |
def __init__(self, latent_regularizer='bvae', beta=100., **kwargs): ''' args: ------ latent_regularizer : str Either 'bvae', 'vae', or 'no' Determines whether regularization is applied to the latent space representation. beta : float beta > 1, used for 'bvae' latent_regularizer, (Unused if 'bvae' not selected) ------ ex. sample = SampleLayer('bvae', 16)([mean, logvar]) ''' if latent_regularizer.lower() in ['bvae', 'vae']: self.reg = latent_regularizer else: self.reg = None if self.reg == 'bvae': self.beta = beta elif self.reg == 'vae': self.beta = 1. super(SampleLayer, self).__init__(**kwargs)
Example #6
Source File: sample_layer.py From BVAE-tf with The Unlicense | 5 votes |
def call(self, x, training=None): if len(x) != 2: raise Exception('input layers must be a list: mean and logvar') if len(x[0].shape) != 2 or len(x[1].shape) != 2: raise Exception('input shape is not a vector [batchSize, latentSize]') mean = x[0] logvar = x[1] # trick to allow setting batch at train/eval time if mean.shape[0].value == None or logvar.shape[0].value == None: return mean + 0*logvar # Keras needs the *0 so the gradinent is not None if self.reg is not None: # kl divergence: latent_loss = -0.5 * (1 + logvar - K.square(mean) - K.exp(logvar)) latent_loss = K.sum(latent_loss, axis=-1) # sum over latent dimension latent_loss = K.mean(latent_loss, axis=0) # avg over batch # use beta to force less usage of vector space: latent_loss = self.beta * latent_loss self.add_loss(latent_loss, x) def reparameterization_trick(): epsilon = K.random_normal(shape=logvar.shape, mean=0., stddev=1.) stddev = K.exp(logvar*0.5) return mean + stddev * epsilon return K.in_train_phase(reparameterization_trick, mean + 0*logvar, training=training) # TODO figure out why this is not working in the specified tf version???
Example #7
Source File: cmvn.py From delta with Apache License 2.0 | 5 votes |
def compute_cmvn(sums, square, count): ''' compute global feature mean and variance vars = E(x^2) - (E(x))^2 ''' mean = sums / count var = (square / count) - np.square(mean) return mean, var
Example #8
Source File: cmvn.py From delta with Apache License 2.0 | 5 votes |
def load_cmvn(path): ''' load mean and variance from cmvn.npy, then convert to TF Tensor ''' # [1, nbins, nchannels] mean, variance = np.load(path) # [1, 1, nbins, nchannels] mean = np.expand_dims(mean, axis=0) variance = np.expand_dims(variance, axis=0) mean = tf.convert_to_tensor(mean, dtype=tf.float32, name='cmvn_mean') variance = tf.convert_to_tensor( variance, dtype=tf.float32, name='cmvn_variance') return mean, variance
Example #9
Source File: cmvn.py From delta with Apache License 2.0 | 5 votes |
def apply_cmvn(feats, mean, variance, epsilon=1e-9): ''' TF: apply CMVN on feature''' return (feats - mean) * tf.rsqrt(variance + epsilon)
Example #10
Source File: cmvn.py From delta with Apache License 2.0 | 5 votes |
def apply_local_cmvn(feats, epsilon=1e-9): ''' feats: (NHWC) ''' mean = tf.expand_dims(keras_backend.mean(feats, axis=1), axis=1) var = tf.expand_dims(keras_backend.var(feats, axis=1), axis=1) feats = (feats - mean) * tf.rsqrt(var + epsilon) return feats
Example #11
Source File: normalization.py From DeepCTR with Apache License 2.0 | 5 votes |
def call(self, inputs): mean = K.mean(inputs, axis=self.axis, keepdims=True) variance = K.mean(K.square(inputs - mean), axis=-1, keepdims=True) std = K.sqrt(variance + self.eps) outputs = (inputs - mean) / std if self.scale: outputs *= self.gamma if self.center: outputs += self.beta return outputs