Python keras.layers.Dense() Examples
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code examples of keras.layers.Dense().
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Example #1
Source Project: keras-anomaly-detection Author: chen0040 File: recurrent.py License: MIT License | 16 votes |
def create_model(time_window_size, metric): model = Sequential() model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu', input_shape=(time_window_size, 1))) model.add(MaxPooling1D(pool_size=4)) model.add(LSTM(64)) model.add(Dense(units=time_window_size, activation='linear')) model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric]) # model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric]) # model.compile(optimizer="sgd", loss="mse", metrics=[metric]) print(model.summary()) return model
Example #2
Source Project: Image-Caption-Generator Author: dabasajay File: model.py License: 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 #3
Source Project: Keras-GAN Author: eriklindernoren File: sgan.py License: MIT License | 8 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(BatchNormalization(momentum=0.8)) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(Activation("relu")) model.add(BatchNormalization(momentum=0.8)) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(Activation("relu")) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(1, kernel_size=3, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example #4
Source Project: vergeml Author: mme File: imagenet.py License: MIT License | 7 votes |
def _makenet(x, num_layers, dropout, random_seed): from keras.layers import Dense, Dropout dropout_seeder = random.Random(random_seed) for i in range(num_layers - 1): # add intermediate layers if dropout: x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x) x = Dense(1024, activation="relu", name='dense_layer_{}'.format(i))(x) if dropout: # add the final dropout layer x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x) return x
Example #5
Source Project: Deep_Learning_Weather_Forecasting Author: BruceBinBoxing File: weather_model.py License: 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 #6
Source Project: Keras-GAN Author: eriklindernoren File: context_encoder.py License: MIT License | 7 votes |
def build_discriminator(self): model = Sequential() model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(256, kernel_size=3, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.missing_shape) validity = model(img) return Model(img, validity)
Example #7
Source Project: Jtyoui Author: jtyoui File: cnn_rnn_crf.py License: MIT License | 7 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 #8
Source Project: keras-anomaly-detection Author: chen0040 File: feedforward.py License: MIT License | 6 votes |
def create_model(self, input_dim): encoding_dim = 14 input_layer = Input(shape=(input_dim,)) encoder = Dense(encoding_dim, activation="tanh", activity_regularizer=regularizers.l1(10e-5))(input_layer) encoder = Dense(encoding_dim // 2, activation="relu")(encoder) decoder = Dense(encoding_dim // 2, activation='tanh')(encoder) decoder = Dense(input_dim, activation='relu')(decoder) model = Model(inputs=input_layer, outputs=decoder) model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy']) return model
Example #9
Source Project: Deep_Learning_Weather_Forecasting Author: BruceBinBoxing File: weather_model.py License: Apache License 2.0 | 6 votes |
def CausalCNN(n_filters, lr, decay, loss, seq_len, input_features, strides_len, kernel_size, dilation_rates): inputs = Input(shape=(seq_len, input_features), name='input_layer') x=inputs for dilation_rate in dilation_rates: x = Conv1D(filters=n_filters, kernel_size=kernel_size, padding='causal', dilation_rate=dilation_rate, activation='linear')(x) x = BatchNormalization()(x) x = Activation('relu')(x) #x = Dense(7, activation='relu', name='dense_layer')(x) outputs = Dense(3, activation='sigmoid', name='output_layer')(x) causalcnn = Model(inputs, outputs=[outputs]) return causalcnn
Example #10
Source Project: Deep_Learning_Weather_Forecasting Author: BruceBinBoxing File: weather_model.py License: Apache License 2.0 | 6 votes |
def weather_ae(layers, lr, decay, loss, input_len, input_features): inputs = Input(shape=(input_len, input_features), name='input_layer') for i, hidden_nums in enumerate(layers): if i==0: hn = Dense(hidden_nums, activation='relu')(inputs) else: hn = Dense(hidden_nums, activation='relu')(hn) outputs = Dense(3, activation='sigmoid', name='output_layer')(hn) weather_model = Model(inputs, outputs=[outputs]) return weather_model
Example #11
Source Project: cnn-levelset Author: wiseodd File: localizer.py License: MIT License | 6 votes |
def __init__(self, model_path=None): if model_path is not None: self.model = self.load_model(model_path) else: # VGG16 last conv features inputs = Input(shape=(7, 7, 512)) x = Convolution2D(128, 1, 1)(inputs) x = Flatten()(x) # Cls head h_cls = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x) h_cls = Dropout(p=0.5)(h_cls) cls_head = Dense(20, activation='softmax', name='cls')(h_cls) # Reg head h_reg = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x) h_reg = Dropout(p=0.5)(h_reg) reg_head = Dense(4, activation='linear', name='reg')(h_reg) # Joint model self.model = Model(input=inputs, output=[cls_head, reg_head])
Example #12
Source Project: Keras-GAN Author: eriklindernoren File: ccgan.py License: MIT License | 6 votes |
def build_discriminator(self): img = Input(shape=self.img_shape) model = Sequential() model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape)) model.add(LeakyReLU(alpha=0.8)) model.add(Conv2D(128, kernel_size=4, strides=2, padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(InstanceNormalization()) model.add(Conv2D(256, kernel_size=4, strides=2, padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(InstanceNormalization()) model.summary() img = Input(shape=self.img_shape) features = model(img) validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features) label = Flatten()(features) label = Dense(self.num_classes+1, activation="softmax")(label) return Model(img, [validity, label])
Example #13
Source Project: Keras-GAN Author: eriklindernoren File: bigan.py License: MIT License | 6 votes |
def build_encoder(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(self.latent_dim)) model.summary() img = Input(shape=self.img_shape) z = model(img) return Model(img, z)
Example #14
Source Project: Keras-GAN Author: eriklindernoren File: bigan.py License: 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 #15
Source Project: Keras-GAN Author: eriklindernoren File: pixelda.py License: MIT License | 6 votes |
def build_classifier(self): def clf_layer(layer_input, filters, f_size=4, normalization=True): """Classifier layer""" d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) d = LeakyReLU(alpha=0.2)(d) if normalization: d = InstanceNormalization()(d) return d img = Input(shape=self.img_shape) c1 = clf_layer(img, self.cf, normalization=False) c2 = clf_layer(c1, self.cf*2) c3 = clf_layer(c2, self.cf*4) c4 = clf_layer(c3, self.cf*8) c5 = clf_layer(c4, self.cf*8) class_pred = Dense(self.num_classes, activation='softmax')(Flatten()(c5)) return Model(img, class_pred)
Example #16
Source Project: Keras-GAN Author: eriklindernoren File: wgan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(self.channels, kernel_size=4, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example #17
Source Project: Keras-GAN Author: eriklindernoren File: wgan_gp.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(self.channels, kernel_size=4, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example #18
Source Project: Keras-GAN Author: eriklindernoren File: lsgan.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) # (!!!) No softmax model.add(Dense(1)) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example #19
Source Project: Keras-GAN Author: eriklindernoren File: cogan.py License: MIT License | 6 votes |
def build_discriminators(self): img1 = Input(shape=self.img_shape) img2 = Input(shape=self.img_shape) # Shared discriminator layers model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) img1_embedding = model(img1) img2_embedding = model(img2) # Discriminator 1 validity1 = Dense(1, activation='sigmoid')(img1_embedding) # Discriminator 2 validity2 = Dense(1, activation='sigmoid')(img2_embedding) return Model(img1, validity1), Model(img2, validity2)
Example #20
Source Project: Keras-GAN Author: eriklindernoren File: dcgan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(self.channels, kernel_size=3, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example #21
Source Project: Keras-GAN Author: eriklindernoren File: gan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(256, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example #22
Source Project: Keras-GAN Author: eriklindernoren File: gan.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example #23
Source Project: Keras-GAN Author: eriklindernoren File: aae.py License: MIT License | 6 votes |
def build_decoder(self): model = Sequential() model.add(Dense(512, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() z = Input(shape=(self.latent_dim,)) img = model(z) return Model(z, img)
Example #24
Source Project: Keras-GAN Author: eriklindernoren File: bgan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(256, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example #25
Source Project: Keras-GAN Author: eriklindernoren File: bgan.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example #26
Source Project: tartarus Author: sergiooramas File: models.py License: MIT License | 6 votes |
def get_model_41(params): embedding_weights = pickle.load(open("../data/datasets/train_data/embedding_weights_w2v-google_MSD-AG.pk","rb")) # main sequential model model = Sequential() model.add(Embedding(len(embedding_weights[0]), params['embedding_dim'], input_length=params['sequence_length'], weights=embedding_weights)) #model.add(Dropout(params['dropout_prob'][0], input_shape=(params['sequence_length'], params['embedding_dim']))) model.add(LSTM(2048)) #model.add(Dropout(params['dropout_prob'][1])) model.add(Dense(output_dim=params["n_out"], init="uniform")) model.add(Activation(params['final_activation'])) logging.debug("Output CNN: %s" % str(model.output_shape)) if params['final_activation'] == 'linear': model.add(Lambda(lambda x :K.l2_normalize(x, axis=1))) return model # CRNN Arch for audio
Example #27
Source Project: Jtyoui Author: jtyoui File: NER.py License: MIT License | 6 votes |
def train_model(): if cxl_model: embedding_matrix = load_embedding() else: embedding_matrix = {} train, label = vocab_train_label(train_path, vocab=vocab, tags=tag, max_chunk_length=length) n = np.array(label, dtype=np.float) labels = n.reshape((n.shape[0], n.shape[1], 1)) model = Sequential([ Embedding(input_dim=len(vocab), output_dim=300, mask_zero=True, input_length=length, weights=[embedding_matrix], trainable=False), SpatialDropout1D(0.2), Bidirectional(layer=LSTM(units=150, return_sequences=True, dropout=0.2, recurrent_dropout=0.2)), TimeDistributed(Dense(len(tag), activation=relu)), ]) crf_ = CRF(units=len(tag), sparse_target=True) model.add(crf_) model.compile(optimizer=Adam(), loss=crf_.loss_function, metrics=[crf_.accuracy]) model.fit(x=np.array(train), y=labels, batch_size=16, epochs=4, callbacks=[RemoteMonitor()]) model.save(model_path)
Example #28
Source Project: armchair-expert Author: csvance File: reaction.py License: MIT License | 6 votes |
def __init__(self, path: str = None, use_gpu=False): import tensorflow as tf from keras.models import Sequential from keras.layers import Dense from keras.backend import set_session self.model = Sequential() self.model.add(Dense(AOLReactionFeatureAnalyzer.NUM_FEATURES, activation='relu', input_dim=AOLReactionFeatureAnalyzer.NUM_FEATURES)) self.model.add(Dense(AOLReactionFeatureAnalyzer.NUM_FEATURES - 2, activation='relu')) self.model.add(Dense(1, activation='sigmoid')) self.model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) if use_gpu: config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config))
Example #29
Source Project: armchair-expert Author: csvance File: structure.py License: MIT License | 6 votes |
def __init__(self, use_gpu: bool = False): import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Embedding from keras.layers import LSTM from keras.backend import set_session latent_dim = StructureModel.SEQUENCE_LENGTH * 8 model = Sequential() model.add( Embedding(StructureFeatureAnalyzer.NUM_FEATURES, StructureFeatureAnalyzer.NUM_FEATURES, input_length=StructureModel.SEQUENCE_LENGTH)) model.add(LSTM(latent_dim, dropout=0.2, return_sequences=False)) model.add(Dense(StructureFeatureAnalyzer.NUM_FEATURES, activation='softmax')) model.summary() model.compile(loss='sparse_categorical_crossentropy', optimizer='adam') self.model = model if use_gpu: config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config))
Example #30
Source Project: Keras-BiGAN Author: manicman1999 File: bigan.py License: MIT License | 6 votes |
def encoder(self): if self.E: return self.E inp = Input(shape = [im_size, im_size, 3]) x = d_block(inp, 1 * cha) #64 x = d_block(x, 2 * cha) #32 x = d_block(x, 3 * cha) #16 x = d_block(x, 4 * cha) #8 x = d_block(x, 8 * cha) #4 x = d_block(x, 16 * cha, p = False) #4 x = Flatten()(x) x = Dense(16 * cha, kernel_initializer = 'he_normal')(x) x = LeakyReLU(0.2)(x) x = Dense(latent_size, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x) self.E = Model(inputs = inp, outputs = x) return self.E