def test_linear(): ''' This function does no input validation, it just returns the thing that was passed in. ''' xs = [1, 5, True, None, 'foo'] for x in xs: assert(x == activations.linear(x))
def test_linear(): ''' This function does no input validation, it just returns the thing that was passed in. ''' xs = [1, 5, True, None, 'foo'] for x in xs: assert (x == activations.linear(x))
def compute_similarity(self, repeated_context_vectors, repeated_query_vectors): element_wise_multiply = repeated_context_vectors * repeated_query_vectors concatenated_tensor = K.concatenate([ repeated_context_vectors, repeated_query_vectors, element_wise_multiply ], axis=-1) dot_product = K.squeeze(K.dot(concatenated_tensor, self.kernel), axis=-1) return linear(dot_product + self.bias)
def build_model(): model = models.Sequential() model.add( LSTM(100, activation=activations.relu(), input_shape=(timeSteps, 1))) model.add(Dropout(1)) #model.add(layers.Dense(20, activation='relu')) model.add(layers.Dense(1, activation=activations.linear())) print("made it2") model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001), loss=tf.keras.losses.mse, metrics=['acc']) return model
def InvertedRes(input, expansion): ''' Args: input: input tensor expansion: expand filters size Output: output: output tensor ''' #Pointwise Convolution x = Conv2D(expansion*3,(1,1), padding='same')(input) x = BatchNormalization()(x) x = ReLU()(x) #Depthwise Convolution x = DepthwiseConv2D((3,3), padding='same')(x) x = BatchNormalization()(x) x = ReLU()(x) #Pointwise Convolution x = Conv2D(3,(1,1))(x) x = BatchNormalization()(x) x = linear(x) x = Add()([x, input]) return x
def test_linear(self): x = np.random.random((10, 5)) self.assertAllClose(x, activations.linear(x))
sns.set() plt.plot(net, act) plt.ylabel('act', fontsize=20) plt.xlabel('net', fontsize=20) plt.title(title, fontsize=20) plt.savefig('./{0}.jpg'.format(title), dpi=300) plt.close() # create a numpy array - TensorFlow defaults to single precision floating point netnp = np.linspace(-5.0, 5.0, 1000, dtype='float32') # convert to a TensorFlow tensor nettf = tf.convert_to_tensor(netnp) # linear activation function acttf = kact.linear(nettf) # need to convert from TensorFlow tensors to numpy arrays before plotting # eval() is called because TensorFlow tensors have no values until they are "run" plt_act(nettf.eval(), acttf.eval(), 'linear activation function') # relu activation function acttf = kact.relu(nettf) plt_act(nettf.eval(), acttf.eval(), 'rectified linear (relu)') # sigmoid activation function acttf = kact.sigmoid(nettf) plt_act(nettf.eval(), acttf.eval(), 'sigmoid') # hard sigmoid activation function acttf = kact.hard_sigmoid(nettf) plt_act(nettf.eval(), acttf.eval(), 'hard sigmoid')
def test_linear(): xs = [1, 5, True, None] for x in xs: assert (x == activations.linear(x))
def test_linear(): xs = [1, 5, True, None] for x in xs: assert(x == activations.linear(x))
def testLinearity(): testValues = [1, 5, True, None] for x in testValues: assert (x == activations.linear(x))
def call(self, inputs, training=False, mask=None): x = nn.relu(self.fc1(inputs)) x = nn.relu(self.fc2(x)) x = linear(self.fc3(x)) return x