我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用keras.activations.relu()。
def wavenetBlock(n_atrous_filters, atrous_filter_size, atrous_rate, n_conv_filters, conv_filter_size): def f(input_): residual = input_ tanh_out = AtrousConvolution1D(n_atrous_filters, atrous_filter_size, atrous_rate=atrous_rate, border_mode='same', activation='tanh')(input_) sigmoid_out = AtrousConvolution1D(n_atrous_filters, atrous_filter_size, atrous_rate=atrous_rate, border_mode='same', activation='sigmoid')(input_) merged = merge([tanh_out, sigmoid_out], mode='mul') skip_out = Convolution1D(1, 1, activation='relu', border_mode='same')(merged) out = merge([skip_out, residual], mode='sum') return out, skip_out return f
def get_basic_generative_model(input_size): input = Input(shape=(1, input_size, 1)) l1a, l1b = wavenetBlock(10, 5, 2, 1, 3)(input) l2a, l2b = wavenetBlock(1, 2, 4, 1, 3)(l1a) l3a, l3b = wavenetBlock(1, 2, 8, 1, 3)(l2a) l4a, l4b = wavenetBlock(1, 2, 16, 1, 3)(l3a) l5a, l5b = wavenetBlock(1, 2, 32, 1, 3)(l4a) l6 = merge([l1b, l2b, l3b, l4b, l5b], mode='sum') l7 = Lambda(relu)(l6) l8 = Convolution2D(1, 1, 1, activation='relu')(l7) l9 = Convolution2D(1, 1, 1)(l8) l10 = Flatten()(l9) l11 = Dense(1, activation='tanh')(l10) model = Model(input=input, output=l11) model.compile(loss='mse', optimizer='rmsprop', metrics=['accuracy']) model.summary() return model
def test_relu(): ''' Relu implementation doesn't depend on the value being a theano variable. Testing ints, floats and theano tensors. ''' from keras.activations import relu as r assert r(5) == 5 assert r(-5) == 0 assert r(-0.1) == 0 assert r(0.1) == 0.1 x = T.vector() exp = r(x) f = theano.function([x], exp) test_values = get_standard_values() result = f(test_values) list_assert_equal(result, test_values) # because no negatives in test values
def test_relu(): ''' Relu implementation doesn't depend on the value being a theano variable. Testing ints, floats and theano tensors. ''' x = K.placeholder(ndim=2) f = K.function([x], [activations.relu(x)]) test_values = get_standard_values() result = f([test_values])[0] # because no negatives in test values assert_allclose(result, test_values, rtol=1e-05)
def temporal_convs_linear(n_nodes, conv_len, n_classes, n_feat, max_len, causal=False, loss='categorical_crossentropy', optimizer='adam', return_param_str=False): """ Used in paper: Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation Lea et al. ECCV 2016 Note: Spatial dropout was not used in the original paper. It tends to improve performance a little. """ inputs = Input(shape=(max_len,n_feat)) if causal: model = ZeroPadding1D((conv_len//2,0))(model) model = Convolution1D(n_nodes, conv_len, input_dim=n_feat, input_length=max_len, border_mode='same', activation='relu')(inputs) if causal: model = Cropping1D((0,conv_len//2))(model) model = SpatialDropout1D(0.3)(model) model = TimeDistributed(Dense(n_classes, activation="softmax" ))(model) model = Model(input=inputs, output=model) model.compile(loss=loss, optimizer=optimizer, sample_weight_mode="temporal") if return_param_str: param_str = "tConv_C{}".format(conv_len) if causal: param_str += "_causal" return model, param_str else: return model
def ED_TCN(n_nodes, conv_len, n_classes, n_feat, max_len, loss='categorical_crossentropy', causal=False, optimizer="rmsprop", activation='norm_relu', return_param_str=False): n_layers = len(n_nodes) inputs = Input(shape=(max_len,n_feat)) model = inputs # ---- Encoder ---- for i in range(n_layers): # Pad beginning of sequence to prevent usage of future data if causal: model = ZeroPadding1D((conv_len//2,0))(model) model = Convolution1D(n_nodes[i], conv_len, border_mode='same')(model) if causal: model = Cropping1D((0,conv_len//2))(model) model = SpatialDropout1D(0.3)(model) if activation=='norm_relu': model = Activation('relu')(model) model = Lambda(channel_normalization, name="encoder_norm_{}".format(i))(model) elif activation=='wavenet': model = WaveNet_activation(model) else: model = Activation(activation)(model) model = MaxPooling1D(2)(model) # ---- Decoder ---- for i in range(n_layers): model = UpSampling1D(2)(model) if causal: model = ZeroPadding1D((conv_len//2,0))(model) model = Convolution1D(n_nodes[-i-1], conv_len, border_mode='same')(model) if causal: model = Cropping1D((0,conv_len//2))(model) model = SpatialDropout1D(0.3)(model) if activation=='norm_relu': model = Activation('relu')(model) model = Lambda(channel_normalization, name="decoder_norm_{}".format(i))(model) elif activation=='wavenet': model = WaveNet_activation(model) else: model = Activation(activation)(model) # Output FC layer model = TimeDistributed(Dense(n_classes, activation="softmax" ))(model) model = Model(input=inputs, output=model) model.compile(loss=loss, optimizer=optimizer, sample_weight_mode="temporal", metrics=['accuracy']) if return_param_str: param_str = "ED-TCN_C{}_L{}".format(conv_len, n_layers) if causal: param_str += "_causal" return model, param_str else: return model
def ED_TCN_atrous(n_nodes, conv_len, n_classes, n_feat, max_len, loss='categorical_crossentropy', causal=False, optimizer="rmsprop", activation='norm_relu', return_param_str=False): n_layers = len(n_nodes) inputs = Input(shape=(None,n_feat)) model = inputs # ---- Encoder ---- for i in range(n_layers): # Pad beginning of sequence to prevent usage of future data if causal: model = ZeroPadding1D((conv_len//2,0))(model) model = AtrousConvolution1D(n_nodes[i], conv_len, atrous_rate=i+1, border_mode='same')(model) if causal: model = Cropping1D((0,conv_len//2))(model) model = SpatialDropout1D(0.3)(model) if activation=='norm_relu': model = Activation('relu')(model) model = Lambda(channel_normalization, name="encoder_norm_{}".format(i))(model) elif activation=='wavenet': model = WaveNet_activation(model) else: model = Activation(activation)(model) # ---- Decoder ---- for i in range(n_layers): if causal: model = ZeroPadding1D((conv_len//2,0))(model) model = AtrousConvolution1D(n_nodes[-i-1], conv_len, atrous_rate=n_layers-i, border_mode='same')(model) if causal: model = Cropping1D((0,conv_len//2))(model) model = SpatialDropout1D(0.3)(model) if activation=='norm_relu': model = Activation('relu')(model) model = Lambda(channel_normalization, name="decoder_norm_{}".format(i))(model) elif activation=='wavenet': model = WaveNet_activation(model) else: model = Activation(activation)(model) # Output FC layer model = TimeDistributed(Dense(n_classes, activation="softmax" ))(model) model = Model(input=inputs, output=model) model.compile(loss=loss, optimizer=optimizer, sample_weight_mode="temporal", metrics=['accuracy']) if return_param_str: param_str = "ED-TCNa_C{}_L{}".format(conv_len, n_layers) if causal: param_str += "_causal" return model, param_str else: return model
def TimeDelayNeuralNetwork(n_nodes, conv_len, n_classes, n_feat, max_len, loss='categorical_crossentropy', causal=False, optimizer="rmsprop", activation='sigmoid', return_param_str=False): # Time-delay neural network n_layers = len(n_nodes) inputs = Input(shape=(max_len,n_feat)) model = inputs inputs_mask = Input(shape=(max_len,1)) model_masks = [inputs_mask] # ---- Encoder ---- for i in range(n_layers): # Pad beginning of sequence to prevent usage of future data if causal: model = ZeroPadding1D((conv_len//2,0))(model) model = AtrousConvolution1D(n_nodes[i], conv_len, atrous_rate=i+1, border_mode='same')(model) # model = SpatialDropout1D(0.3)(model) if causal: model = Cropping1D((0,conv_len//2))(model) if activation=='norm_relu': model = Activation('relu')(model) model = Lambda(channel_normalization, name="encoder_norm_{}".format(i))(model) elif activation=='wavenet': model = WaveNet_activation(model) else: model = Activation(activation)(model) # Output FC layer model = TimeDistributed(Dense(n_classes, activation="softmax"))(model) model = Model(input=inputs, output=model) model.compile(loss=loss, optimizer=optimizer, sample_weight_mode="temporal", metrics=['accuracy']) if return_param_str: param_str = "TDN_C{}".format(conv_len) if causal: param_str += "_causal" return model, param_str else: return model
