Python keras.layers.concatenate() Examples
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Example #1
Source File: model.py From Image-Caption-Generator with MIT License | 11 votes |
def RNNModel(vocab_size, max_len, rnnConfig, model_type): embedding_size = rnnConfig['embedding_size'] if model_type == 'inceptionv3': # InceptionV3 outputs a 2048 dimensional vector for each image, which we'll feed to RNN Model image_input = Input(shape=(2048,)) elif model_type == 'vgg16': # VGG16 outputs a 4096 dimensional vector for each image, which we'll feed to RNN Model image_input = Input(shape=(4096,)) image_model_1 = Dropout(rnnConfig['dropout'])(image_input) image_model = Dense(embedding_size, activation='relu')(image_model_1) caption_input = Input(shape=(max_len,)) # mask_zero: We zero pad inputs to the same length, the zero mask ignores those inputs. E.g. it is an efficiency. caption_model_1 = Embedding(vocab_size, embedding_size, mask_zero=True)(caption_input) caption_model_2 = Dropout(rnnConfig['dropout'])(caption_model_1) caption_model = LSTM(rnnConfig['LSTM_units'])(caption_model_2) # Merging the models and creating a softmax classifier final_model_1 = concatenate([image_model, caption_model]) final_model_2 = Dense(rnnConfig['dense_units'], activation='relu')(final_model_1) final_model = Dense(vocab_size, activation='softmax')(final_model_2) model = Model(inputs=[image_input, caption_input], outputs=final_model) model.compile(loss='categorical_crossentropy', optimizer='adam') return model
Example #2
Source File: weather_model.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 7 votes |
def weather_l2(hidden_nums=100,l2=0.01): input_img = Input(shape=(37,)) hn = Dense(hidden_nums, activation='relu')(input_img) hn = Dense(hidden_nums, activation='relu', kernel_regularizer=regularizers.l2(l2))(hn) out_u = Dense(37, activation='sigmoid', name='ae_part')(hn) out_sig = Dense(37, activation='linear', name='pred_part')(hn) out_both = concatenate([out_u, out_sig], axis=1, name = 'concatenate') #weather_model = Model(input_img, outputs=[out_ae, out_pred]) mve_model = Model(input_img, outputs=[out_both]) mve_model.compile(optimizer='adam', loss=mve_loss, loss_weights=[1.]) return mve_model
Example #3
Source File: squeezenet.py From Deep-Learning-with-TensorFlow-Second-Edition with MIT License | 6 votes |
def fire_module(x, fire_id, squeeze=16, expand=64): s_id = 'fire' + str(fire_id) + '/' if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = 3 x = Conv2D(squeeze, (1, 1), padding='valid', name=s_id + sq1x1)(x) x = Activation('relu', name=s_id + relu + sq1x1)(x) left = Conv2D(expand, (1, 1), padding='valid', name=s_id + exp1x1)(x) left = Activation('relu', name=s_id + relu + exp1x1)(left) right = Conv2D(expand, (3, 3), padding='same', name=s_id + exp3x3)(x) right = Activation('relu', name=s_id + relu + exp3x3)(right) x = concatenate([left, right], axis=channel_axis, name=s_id + 'concat') return x # Original SqueezeNet from paper.
Example #4
Source File: squeezeDet.py From squeezedet-keras with MIT License | 6 votes |
def _pad(self, input): """ pads the network output so y_pred and y_true have the same dimensions :param input: previous layer :return: layer, last dimensions padded for 4 """ #pad = K.placeholder( (None,self.config.ANCHORS, 4)) #pad = np.zeros ((self.config.BATCH_SIZE,self.config.ANCHORS, 4)) #return K.concatenate( [input, pad], axis=-1) padding = np.zeros((3,2)) padding[2,1] = 4 return tf.pad(input, padding ,"CONSTANT") #loss function to optimize
Example #5
Source File: models.py From DigiX_HuaWei_Population_Age_Attribution_Predict with MIT License | 6 votes |
def CapsuleNet_v2(n_capsule = 10, n_routings = 5, capsule_dim = 16, n_recurrent=100, dropout_rate=0.2, l2_penalty=0.0001): K.clear_session() inputs = Input(shape=(200,)) x = Embedding(20000, 300, trainable=True)(inputs) x = SpatialDropout1D(dropout_rate)(x) x = Bidirectional( CuDNNGRU(n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x) x = PReLU()(x) x = Capsule( num_capsule=n_capsule, dim_capsule=capsule_dim, routings=n_routings, share_weights=True)(x) x = Flatten(name = 'concatenate')(x) x = Dropout(dropout_rate)(x) # fc = Dense(128, activation='sigmoid')(x) outputs = Dense(6, activation='softmax')(x) model = Model(inputs=inputs, outputs=outputs) model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy']) return model
Example #6
Source File: models.py From DigiX_HuaWei_Population_Age_Attribution_Predict with MIT License | 6 votes |
def CapsuleNet(n_capsule = 10, n_routings = 5, capsule_dim = 16, n_recurrent=100, dropout_rate=0.2, l2_penalty=0.0001): K.clear_session() inputs = Input(shape=(170,)) x = Embedding(21099, 300, trainable=True)(inputs) x = SpatialDropout1D(dropout_rate)(x) x = Bidirectional( CuDNNGRU(n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x) x = PReLU()(x) x = Capsule( num_capsule=n_capsule, dim_capsule=capsule_dim, routings=n_routings, share_weights=True)(x) x = Flatten(name = 'concatenate')(x) x = Dropout(dropout_rate)(x) # fc = Dense(128, activation='sigmoid')(x) outputs = Dense(6, activation='softmax')(x) model = Model(inputs=inputs, outputs=outputs) model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy']) return model
Example #7
Source File: networks.py From posewarp-cvpr2018 with MIT License | 6 votes |
def network_unet(param): n_joints = param['n_joints'] pose_dn = param['posemap_downsample'] img_h = param['IMG_HEIGHT'] img_w = param['IMG_WIDTH'] src_in = Input(shape=(img_h, img_w, 3)) pose_src = Input(shape=(img_h / pose_dn, img_w / pose_dn, n_joints)) pose_tgt = Input(shape=(img_h / pose_dn, img_w / pose_dn, n_joints)) x = unet(src_in, concatenate([pose_src, pose_tgt]), [64] + [128] * 3 + [256] * 7, [256, 256, 256, 128, 64]) y = my_conv(x, 3, activation='tanh') model = Model(inputs=[src_in, pose_src, pose_tgt], outputs=[y]) return model
Example #8
Source File: example.py From learning2run with MIT License | 6 votes |
def preprocess(x): return K.concatenate([ x[:,:,0:1] / 360.0, x[:,:,1:3], x[:,:,3:4] / 360.0, x[:,:,4:6], x[:,:,6:18] / 360.0, x[:,:,18:19] - x[:,:,1:2], x[:,:,19:22], x[:,:,28:29] - x[:,:,1:2], x[:,:,29:30], x[:, :, 30:31] - x[:, :, 1:2], x[:, :, 31:32], x[:, :, 32:33] - x[:, :, 1:2], x[:, :, 33:34], x[:, :, 34:35] - x[:, :, 1:2], x[:, :, 35:41], ], axis=2)
Example #9
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_tiny_concat_random(self): np.random.seed(1988) input_dim = 10 num_channels = 6 # Define a model input_tensor = Input(shape=(input_dim,)) x1 = Dense(num_channels)(input_tensor) x2 = Dense(num_channels)(x1) x3 = Dense(num_channels)(x1) x4 = concatenate([x2, x3]) x5 = Dense(num_channels)(x4) model = Model(inputs=[input_tensor], outputs=[x5]) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Get the coreml model self._test_model(model)
Example #10
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_tiny_concat_seq_random(self): np.random.seed(1988) max_features = 10 embedding_dims = 4 seq_len = 5 num_channels = 6 # Define a model input_tensor = Input(shape=(seq_len,)) x1 = Embedding(max_features, embedding_dims)(input_tensor) x2 = Embedding(max_features, embedding_dims)(input_tensor) x3 = concatenate([x1, x2], axis=1) model = Model(inputs=[input_tensor], outputs=[x3]) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Get the coreml model self._test_model(model, one_dim_seq_flags=[True])
Example #11
Source File: networks.py From posewarp-cvpr2018 with MIT License | 6 votes |
def unet(x_in, pose_in, nf_enc, nf_dec): x0 = my_conv(x_in, nf_enc[0], ks=7) # 256 x1 = my_conv(x0, nf_enc[1], strides=2) # 128 x2 = concatenate([x1, pose_in]) x3 = my_conv(x2, nf_enc[2]) x4 = my_conv(x3, nf_enc[3], strides=2) # 64 x5 = my_conv(x4, nf_enc[4]) x6 = my_conv(x5, nf_enc[5], strides=2) # 32 x7 = my_conv(x6, nf_enc[6]) x8 = my_conv(x7, nf_enc[7], strides=2) # 16 x9 = my_conv(x8, nf_enc[8]) x10 = my_conv(x9, nf_enc[9], strides=2) # 8 x = my_conv(x10, nf_enc[10]) skips = [x9, x7, x5, x3, x0] filters = [nf_enc[10], nf_dec[0], nf_dec[1], nf_dec[2], nf_enc[3]] for i in range(5): out_sz = 8*(2**(i+1)) x = Lambda(interp_upsampling, output_shape = (out_sz, out_sz, filters[i]))(x) x = concatenate([x, skips[i]]) x = my_conv(x, nf_dec[i]) return x
Example #12
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_shared_vision(self): digit_input = Input(shape=(27, 27, 1)) x = Conv2D(64, (3, 3))(digit_input) x = Conv2D(64, (3, 3))(x) out = Flatten()(x) vision_model = Model(inputs=[digit_input], outputs=[out]) # then define the tell-digits-apart model digit_a = Input(shape=(27, 27, 1)) digit_b = Input(shape=(27, 27, 1)) # the vision model will be shared, weights and all out_a = vision_model(digit_a) out_b = vision_model(digit_b) concatenated = concatenate([out_a, out_b]) out = Dense(1, activation="sigmoid")(concatenated) model = Model(inputs=[digit_a, digit_b], outputs=out) model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) self._test_model(model)
Example #13
Source File: squeezenet.py From Model-Playgrounds with MIT License | 6 votes |
def fire_module(x, fire_id, squeeze=16, expand=64): s_id = 'fire' + str(fire_id) + '/' if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = 3 x = Convolution2D(squeeze, (1, 1), padding='valid', name=s_id + sq1x1)(x) x = Activation('relu', name=s_id + relu + sq1x1)(x) left = Convolution2D(expand, (1, 1), padding='valid', name=s_id + exp1x1)(x) left = Activation('relu', name=s_id + relu + exp1x1)(left) right = Convolution2D(expand, (3, 3), padding='same', name=s_id + exp3x3)(x) right = Activation('relu', name=s_id + relu + exp3x3)(right) x = concatenate([left, right], axis=channel_axis, name=s_id + 'concat') return x # Original SqueezeNet from paper.
Example #14
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_tiny_image_captioning(self): # use a conv layer as a image feature branch img_input_1 = Input(shape=(16, 16, 3)) x = Conv2D(2, (3, 3))(img_input_1) x = Flatten()(x) img_model = Model(inputs=[img_input_1], outputs=[x]) img_input = Input(shape=(16, 16, 3)) x = img_model(img_input) x = Dense(8, name="cap_dense")(x) x = Reshape((1, 8), name="cap_reshape")(x) sentence_input = Input(shape=(5,)) # max_length = 5 y = Embedding(8, 8, name="cap_embedding")(sentence_input) z = concatenate([x, y], axis=1, name="cap_merge") z = LSTM(4, return_sequences=True, name="cap_lstm")(z) z = TimeDistributed(Dense(8), name="cap_timedistributed")(z) combined_model = Model(inputs=[img_input, sentence_input], outputs=[z]) self._test_model(combined_model, one_dim_seq_flags=[False, True])
Example #15
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_tiny_image_captioning_feature_merge(self): img_input_1 = Input(shape=(16, 16, 3)) x = Conv2D(2, (3, 3))(img_input_1) x = Flatten()(x) img_model = Model([img_input_1], [x]) img_input = Input(shape=(16, 16, 3)) x = img_model(img_input) x = Dense(8, name="cap_dense")(x) x = Reshape((1, 8), name="cap_reshape")(x) sentence_input = Input(shape=(5,)) # max_length = 5 y = Embedding(8, 8, name="cap_embedding")(sentence_input) z = concatenate([x, y], axis=1, name="cap_merge") combined_model = Model(inputs=[img_input, sentence_input], outputs=[z]) self._test_model(combined_model, one_dim_seq_flags=[False, True])
Example #16
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_dense_elementwise_params(self): options = dict(modes=[add, multiply, concatenate, average, maximum]) def build_model(mode): x1 = Input(shape=(3,)) x2 = Input(shape=(3,)) y1 = Dense(4)(x1) y2 = Dense(4)(x2) z = mode([y1, y2]) model = Model([x1, x2], z) return mode, model product = itertools.product(*options.values()) args = [build_model(p[0]) for p in product] print("Testing a total of %s cases. This could take a while" % len(args)) for param, model in args: self._run_test(model, param)
Example #17
Source File: bigan.py From Keras-GAN with MIT License | 6 votes |
def build_discriminator(self): z = Input(shape=(self.latent_dim, )) img = Input(shape=self.img_shape) d_in = concatenate([z, Flatten()(img)]) model = Dense(1024)(d_in) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.5)(model) model = Dense(1024)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.5)(model) model = Dense(1024)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.5)(model) validity = Dense(1, activation="sigmoid")(model) return Model([z, img], validity)
Example #18
Source File: cnn_rnn_crf.py From Jtyoui with MIT License | 6 votes |
def create_model(): inputs = Input(shape=(length,), dtype='int32', name='inputs') embedding_1 = Embedding(len(vocab), EMBED_DIM, input_length=length, mask_zero=True)(inputs) bilstm = Bidirectional(LSTM(EMBED_DIM // 2, return_sequences=True))(embedding_1) bilstm_dropout = Dropout(DROPOUT_RATE)(bilstm) embedding_2 = Embedding(len(vocab), EMBED_DIM, input_length=length)(inputs) con = Conv1D(filters=FILTERS, kernel_size=2 * HALF_WIN_SIZE + 1, padding='same')(embedding_2) con_d = Dropout(DROPOUT_RATE)(con) dense_con = TimeDistributed(Dense(DENSE_DIM))(con_d) rnn_cnn = concatenate([bilstm_dropout, dense_con], axis=2) dense = TimeDistributed(Dense(len(chunk_tags)))(rnn_cnn) crf = CRF(len(chunk_tags), sparse_target=True) crf_output = crf(dense) model = Model(input=[inputs], output=[crf_output]) model.compile(loss=crf.loss_function, optimizer=Adam(), metrics=[crf.accuracy]) return model
Example #19
Source File: model.py From FaceNet with Apache License 2.0 | 5 votes |
def build_model(): base_model = InceptionResNetV2(include_top=False, weights='imagenet', input_shape=(img_size, img_size, channel), pooling='avg') image_input = base_model.input x = base_model.layers[-1].output out = Dense(embedding_size)(x) image_embedder = Model(image_input, out) input_a = Input((img_size, img_size, channel), name='anchor') input_p = Input((img_size, img_size, channel), name='positive') input_n = Input((img_size, img_size, channel), name='negative') normalize = Lambda(lambda x: K.l2_normalize(x, axis=-1), name='normalize') x = image_embedder(input_a) output_a = normalize(x) x = image_embedder(input_p) output_p = normalize(x) x = image_embedder(input_n) output_n = normalize(x) merged_vector = concatenate([output_a, output_p, output_n], axis=-1) model = Model(inputs=[input_a, input_p, input_n], outputs=merged_vector) return model
Example #20
Source File: FEC.py From FECNet with MIT License | 5 votes |
def inception_module(x,params,concat_axis,padding='same',data_format=DATA_FORMAT,dilation_rate=(1,1),activation='relu',use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,bias_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None,weight_decay=None): (branch1,branch2,branch3,branch4)=params if weight_decay: kernel_regularizer=regularizers.l2(weight_decay) bias_regularizer=regularizers.l2(weight_decay) else: kernel_regularizer=None bias_regularizer=None #1x1 if branch1[1]>0: pathway1=Conv2D(filters=branch1[1],kernel_size=(1,1),strides=branch1[0],padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(x) #1x1->3x3 pathway2=Conv2D(filters=branch2[0],kernel_size=(1,1),strides=1,padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(x) pathway2=Conv2D(filters=branch2[1],kernel_size=(3,3),strides=branch1[0],padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(pathway2) #1x1->5x5 pathway3=Conv2D(filters=branch3[0],kernel_size=(1,1),strides=1,padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(x) pathway3=Conv2D(filters=branch3[1],kernel_size=(5,5),strides=branch1[0],padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(pathway3) #3x3->1x1 pathway4=MaxPooling2D(pool_size=(3,3),strides=branch1[0],padding=padding,data_format=DATA_FORMAT)(x) if branch4[0]>0: pathway4=Conv2D(filters=branch4[0],kernel_size=(1,1),strides=1,padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(pathway4) if branch1[1]>0: return concatenate([pathway1,pathway2,pathway3,pathway4],axis=concat_axis) else: return concatenate([pathway2, pathway3, pathway4], axis=concat_axis)
Example #21
Source File: FECWithPretrained.py From FECNet with MIT License | 5 votes |
def inception_module(x,params,concat_axis,padding='same',data_format=DATA_FORMAT,dilation_rate=(1,1),activation='relu',use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,bias_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None,weight_decay=None): (branch1,branch2,branch3,branch4)=params if weight_decay: kernel_regularizer=regularizers.l2(weight_decay) bias_regularizer=regularizers.l2(weight_decay) else: kernel_regularizer=None bias_regularizer=None #1x1 if branch1[1]>0: pathway1=Conv2D(filters=branch1[1],kernel_size=(1,1),strides=branch1[0],padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(x) #1x1->3x3 pathway2=Conv2D(filters=branch2[0],kernel_size=(1,1),strides=1,padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(x) pathway2=Conv2D(filters=branch2[1],kernel_size=(3,3),strides=branch1[0],padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(pathway2) #1x1->5x5 pathway3=Conv2D(filters=branch3[0],kernel_size=(1,1),strides=1,padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(x) pathway3=Conv2D(filters=branch3[1],kernel_size=(5,5),strides=branch1[0],padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(pathway3) #3x3->1x1 pathway4=MaxPooling2D(pool_size=(3,3),strides=branch1[0],padding=padding,data_format=DATA_FORMAT)(x) if branch4[0]>0: pathway4=Conv2D(filters=branch4[0],kernel_size=(1,1),strides=1,padding=padding,data_format=data_format,dilation_rate=dilation_rate,activation=activation,use_bias=use_bias,kernel_initializer=kernel_initializer,bias_initializer=bias_initializer,kernel_regularizer=kernel_regularizer,bias_regularizer=bias_regularizer,activity_regularizer=activity_regularizer,kernel_constraint=kernel_constraint,bias_constraint=bias_constraint)(pathway4) if branch1[1]>0: return concatenate([pathway1,pathway2,pathway3,pathway4],axis=concat_axis) else: return concatenate([pathway2, pathway3, pathway4], axis=concat_axis)
Example #22
Source File: networks.py From posewarp-cvpr2018 with MIT License | 5 votes |
def discriminator(param): img_h = param['IMG_HEIGHT'] img_w = param['IMG_WIDTH'] n_joints = param['n_joints'] pose_dn = param['posemap_downsample'] x_tgt = Input(shape=(img_h, img_w, 3)) x_src_pose = Input(shape=(img_h / pose_dn, img_w / pose_dn, n_joints)) x_tgt_pose = Input(shape=(img_h / pose_dn, img_w / pose_dn, n_joints)) x = my_conv(x_tgt, 64, ks=5) x = MaxPooling2D()(x) # 128 x = concatenate([x, x_src_pose, x_tgt_pose]) x = my_conv(x, 128, ks=5) x = MaxPooling2D()(x) # 64 x = my_conv(x, 256) x = MaxPooling2D()(x) # 32 x = my_conv(x, 256) x = MaxPooling2D()(x) # 16 x = my_conv(x, 256) x = MaxPooling2D()(x) # 8 x = my_conv(x, 256) # 8 x = Flatten()(x) x = Dense(256, activation='relu')(x) x = Dense(256, activation='relu')(x) y = Dense(1, activation='sigmoid')(x) model = Model(inputs=[x_tgt, x_src_pose, x_tgt_pose], outputs=y, name='discriminator') return model
Example #23
Source File: retain_train.py From retain-keras with Apache License 2.0 | 5 votes |
def __call__(self, w): other_weights = K.cast(K.ones(K.shape(w))[:-1], K.floatx()) last_weight = K.cast(K.equal(K.reshape(w[-1, :], (1, K.shape(w)[1])), 0.), K.floatx()) appended = K.concatenate([other_weights, last_weight], axis=0) w *= appended return w
Example #24
Source File: autoencoder_model.py From UnDeepVO with MIT License | 5 votes |
def deconv_block(self, input, channels, kernel_size, skip): deconv1 = self.deconv(input, channels, kernel_size, 2) if skip is not None: concat1 = concatenate([deconv1, skip], 3) else: concat1 = deconv1 iconv1 = self.conv(concat1, channels, kernel_size, 1) return iconv1
Example #25
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_dangling_merge_right(self): x1 = Input(shape=(4,), name="input1") x2 = Input(shape=(5,), name="input2") y1 = Dense(6, name="dense")(x2) z = concatenate([y1, x1]) model = Model(inputs=[x1, x2], outputs=[z]) model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) self._test_model(model)
Example #26
Source File: keras2_emitter.py From MMdnn with MIT License | 5 votes |
def _layer_Conv(self): self.add_body(0, """ def convolution(weights_dict, name, input, group, conv_type, filters=None, **kwargs): if not conv_type.startswith('layer'): layer = keras.applications.mobilenet.DepthwiseConv2D(name=name, **kwargs)(input) return layer elif conv_type == 'layers.DepthwiseConv2D': layer = layers.DepthwiseConv2D(name=name, **kwargs)(input) return layer inp_filters = K.int_shape(input)[-1] inp_grouped_channels = int(inp_filters / group) out_grouped_channels = int(filters / group) group_list = [] if group == 1: func = getattr(layers, conv_type.split('.')[-1]) layer = func(name = name, filters = filters, **kwargs)(input) return layer weight_groups = list() if not weights_dict == None: w = np.array(weights_dict[name]['weights']) weight_groups = np.split(w, indices_or_sections=group, axis=-1) for c in range(group): x = layers.Lambda(lambda z: z[..., c * inp_grouped_channels:(c + 1) * inp_grouped_channels])(input) x = layers.Conv2D(name=name + "_" + str(c), filters=out_grouped_channels, **kwargs)(x) weights_dict[name + "_" + str(c)] = dict() weights_dict[name + "_" + str(c)]['weights'] = weight_groups[c] group_list.append(x) layer = layers.concatenate(group_list, axis = -1) if 'bias' in weights_dict[name]: b = K.variable(weights_dict[name]['bias'], name = name + "_bias") layer = layer + b return layer""")
Example #27
Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_dangling_merge_left(self): x1 = Input(shape=(4,), name="input1") x2 = Input(shape=(5,), name="input2") y1 = Dense(6, name="dense")(x2) z = concatenate([x1, y1]) model = Model(inputs=[x1, x2], outputs=[z]) model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) self._test_model(model)
Example #28
Source File: keras2_emitter.py From MMdnn with MIT License | 5 votes |
def emit_Concat(self, IR_node, in_scope=False): inputs = ', '.join('%s' % self.parent_variable_name(IR_node, s) for s in IR_node.in_edges) if in_scope: code = "{:<15} = K.concatenate([{}])".format( IR_node.variable_name, inputs) else: code = self._emit_merge(IR_node, "concatenate") return code
Example #29
Source File: retain_train.py From retain-keras with Apache License 2.0 | 5 votes |
def __call__(self, w): other_weights = K.cast(K.greater_equal(w, 0)[:-1], K.floatx()) last_weight = K.cast(K.equal(K.reshape(w[-1, :], (1, K.shape(w)[1])), 0.), K.floatx()) appended = K.concatenate([other_weights, last_weight], axis=0) w *= appended return w
Example #30
Source File: custom_objects.py From keras_mixnets with MIT License | 5 votes |
def call(self, inputs, **kwargs): if len(self._layers) == 1: return self._layers[0](inputs) filters = K.int_shape(inputs)[self._channel_axis] splits = self._split_channels(filters, self.groups) x_splits = tf.split(inputs, splits, self._channel_axis) x_outputs = [c(x) for x, c in zip(x_splits, self._layers)] x = layers.concatenate(x_outputs, axis=self._channel_axis) return x