Python keras.layers.Dropout() Examples
The following are 30 code examples for showing how to use keras.layers.Dropout(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: Image-Caption-Generator Author: dabasajay File: model.py License: MIT License | 8 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
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 3
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 4
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 5
Project: Keras-GAN Author: eriklindernoren File: dualgan.py License: MIT License | 6 votes |
def build_generator(self): X = Input(shape=(self.img_dim,)) model = Sequential() model.add(Dense(256, input_dim=self.img_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dropout(0.4)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dropout(0.4)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dropout(0.4)) model.add(Dense(self.img_dim, activation='tanh')) X_translated = model(X) return Model(X, X_translated)
Example 6
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 7
Project: Jtyoui Author: jtyoui File: cnn_rnn_crf.py License: 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 8
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelA(): model = Sequential() model.add(Conv2D(64, (5, 5), padding='valid')) model.add(Activation('relu')) model.add(Conv2D(64, (5, 5))) model.add(Activation('relu')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 9
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelB(): model = Sequential() model.add(Dropout(0.2, input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Convolution2D(64, 8, 8, subsample=(2, 2), border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(128, 6, 6, subsample=(2, 2), border_mode='valid')) model.add(Activation('relu')) model.add(Convolution2D(128, 5, 5, subsample=(1, 1))) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 10
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelC(): model = Sequential() model.add(Convolution2D(128, 3, 3, border_mode='valid', input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Activation('relu')) model.add(Convolution2D(64, 3, 3)) model.add(Activation('relu')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 11
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelD(): model = Sequential() model.add(Flatten(input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 12
Project: subsync Author: tympanix File: train_ann.py License: Apache License 2.0 | 6 votes |
def ann_model(input_shape): inp = Input(shape=input_shape, name='mfcc_in') model = inp model = Conv1D(filters=12, kernel_size=(3), activation='relu')(model) model = Conv1D(filters=12, kernel_size=(3), activation='relu')(model) model = Flatten()(model) model = Dense(56)(model) model = Activation('relu')(model) model = BatchNormalization()(model) model = Dropout(0.2)(model) model = Dense(28)(model) model = Activation('relu')(model) model = BatchNormalization()(model) model = Dense(1)(model) model = Activation('sigmoid')(model) model = Model(inp, model) return model
Example 13
Project: HDLTex Author: kk7nc File: BuildModel.py License: MIT License | 6 votes |
def buildModel_DNN(Shape, nClasses, nLayers=3,Number_Node=100, dropout=0.5): ''' buildModel_DNN(nFeatures, nClasses, nLayers=3,Numberof_NOde=100, dropout=0.5) Build Deep neural networks (Multi-layer perceptron) Model for text classification Shape is input feature space nClasses is number of classes nLayers is number of hidden Layer Number_Node is number of unit in each hidden layer dropout is dropout value for solving overfitting problem ''' model = Sequential() model.add(Dense(Number_Node, input_dim=Shape)) model.add(Dropout(dropout)) for i in range(0,nLayers): model.add(Dense(Number_Node, activation='relu')) model.add(Dropout(dropout)) model.add(Dense(nClasses, activation='softmax')) model.compile(loss='sparse_categorical_crossentropy', optimizer='RMSprop', metrics=['accuracy']) return model
Example 14
Project: Deep_Learning_Weather_Forecasting Author: BruceBinBoxing File: weather_model.py License: Apache License 2.0 | 5 votes |
def weather_fnn(layers, lr, decay, loss, seq_len, input_features, output_features): ori_inputs = Input(shape=(seq_len, input_features), name='input_layer') #print(seq_len*input_features) conv_ = Conv1D(11, kernel_size=13, strides=1, data_format='channels_last', padding='valid', activation='linear')(ori_inputs) conv_ = BatchNormalization(name='BN_conv')(conv_) conv_ = Activation('relu')(conv_) conv_ = Conv1D(5, kernel_size=7, strides=1, data_format='channels_last', padding='valid', activation='linear')(conv_) conv_ = BatchNormalization(name='BN_conv2')(conv_) conv_ = Activation('relu')(conv_) inputs = Reshape((-1,))(conv_) for i, hidden_nums in enumerate(layers): if i==0: hn = Dense(hidden_nums, activation='linear')(inputs) hn = BatchNormalization(name='BN_{}'.format(i))(hn) hn = Activation('relu')(hn) else: hn = Dense(hidden_nums, activation='linear')(hn) hn = BatchNormalization(name='BN_{}'.format(i))(hn) hn = Activation('relu')(hn) #hn = Dropout(0.1)(hn) #print(seq_len, output_features) #print(hn) outputs = Dense(seq_len*output_features, activation='sigmoid', name='output_layer')(hn) # 37*3 outputs = Reshape((seq_len, output_features))(outputs) weather_fnn = Model(ori_inputs, outputs=[outputs]) return weather_fnn
Example 15
Project: Kaggler Author: jeongyoonlee File: categorical.py License: MIT License | 5 votes |
def _get_model(X, cat_cols, num_cols, n_uniq, n_emb, output_activation): inputs = [] num_inputs = [] embeddings = [] for i, col in enumerate(cat_cols): if not n_uniq[i]: n_uniq[i] = X[col].nunique() if not n_emb[i]: n_emb[i] = max(MIN_EMBEDDING, 2 * int(np.log2(n_uniq[i]))) _input = Input(shape=(1,), name=col) _embed = Embedding(input_dim=n_uniq[i], output_dim=n_emb[i], name=col + EMBEDDING_SUFFIX)(_input) _embed = Dropout(.2)(_embed) _embed = Reshape((n_emb[i],))(_embed) inputs.append(_input) embeddings.append(_embed) if num_cols: num_inputs = Input(shape=(len(num_cols),), name='num_inputs') merged_input = Concatenate(axis=1)(embeddings + [num_inputs]) inputs = inputs + [num_inputs] else: merged_input = Concatenate(axis=1)(embeddings) x = BatchNormalization()(merged_input) x = Dense(128, activation='relu')(x) x = Dropout(.5)(x) x = BatchNormalization()(x) x = Dense(64, activation='relu')(x) x = Dropout(.5)(x) x = BatchNormalization()(x) output = Dense(1, activation=output_activation)(x) model = Model(inputs=inputs, outputs=output) return model, n_emb, n_uniq
Example 16
Project: Keras-GAN Author: eriklindernoren File: sgan.py License: MIT License | 5 votes |
def build_discriminator(self): model = Sequential() model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) 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(Dropout(0.25)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(256, kernel_size=3, strides=1, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.summary() img = Input(shape=self.img_shape) features = model(img) valid = Dense(1, activation="sigmoid")(features) label = Dense(self.num_classes+1, activation="softmax")(features) return Model(img, [valid, label])
Example 17
Project: Keras-GAN Author: eriklindernoren File: context_encoder.py License: MIT License | 5 votes |
def build_generator(self): model = Sequential() # Encoder model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(64, kernel_size=3, strides=2, 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(512, kernel_size=1, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.5)) # Decoder 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(self.channels, kernel_size=3, padding="same")) model.add(Activation('tanh')) model.summary() masked_img = Input(shape=self.img_shape) gen_missing = model(masked_img) return Model(masked_img, gen_missing)
Example 18
Project: Keras-GAN Author: eriklindernoren File: ccgan.py License: MIT License | 5 votes |
def build_generator(self): """U-Net Generator""" def conv2d(layer_input, filters, f_size=4, bn=True): """Layers used during downsampling""" d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) d = LeakyReLU(alpha=0.2)(d) if bn: d = BatchNormalization(momentum=0.8)(d) return d def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0): """Layers used during upsampling""" u = UpSampling2D(size=2)(layer_input) u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u) if dropout_rate: u = Dropout(dropout_rate)(u) u = BatchNormalization(momentum=0.8)(u) u = Concatenate()([u, skip_input]) return u img = Input(shape=self.img_shape) # Downsampling d1 = conv2d(img, self.gf, bn=False) d2 = conv2d(d1, self.gf*2) d3 = conv2d(d2, self.gf*4) d4 = conv2d(d3, self.gf*8) # Upsampling u1 = deconv2d(d4, d3, self.gf*4) u2 = deconv2d(u1, d2, self.gf*2) u3 = deconv2d(u2, d1, self.gf) u4 = UpSampling2D(size=2)(u3) output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4) return Model(img, output_img)
Example 19
Project: Keras-GAN Author: eriklindernoren File: infogan.py License: MIT License | 5 votes |
def build_disk_and_q_net(self): img = Input(shape=self.img_shape) # Shared layers between discriminator and recognition network model = Sequential() model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(256, kernel_size=3, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(512, kernel_size=3, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(BatchNormalization(momentum=0.8)) model.add(Flatten()) img_embedding = model(img) # Discriminator validity = Dense(1, activation='sigmoid')(img_embedding) # Recognition q_net = Dense(128, activation='relu')(img_embedding) label = Dense(self.num_classes, activation='softmax')(q_net) # Return discriminator and recognition network return Model(img, validity), Model(img, label)
Example 20
Project: Keras-GAN Author: eriklindernoren File: wgan.py License: MIT License | 5 votes |
def build_critic(self): model = Sequential() model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(32, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=1, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1)) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example 21
Project: Keras-GAN Author: eriklindernoren File: wgan_gp.py License: MIT License | 5 votes |
def build_critic(self): model = Sequential() model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(32, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=1, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1)) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example 22
Project: Keras-GAN Author: eriklindernoren File: cyclegan.py License: MIT License | 5 votes |
def build_generator(self): """U-Net Generator""" def conv2d(layer_input, filters, f_size=4): """Layers used during downsampling""" d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) d = LeakyReLU(alpha=0.2)(d) d = InstanceNormalization()(d) return d def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0): """Layers used during upsampling""" u = UpSampling2D(size=2)(layer_input) u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u) if dropout_rate: u = Dropout(dropout_rate)(u) u = InstanceNormalization()(u) u = Concatenate()([u, skip_input]) return u # Image input d0 = Input(shape=self.img_shape) # Downsampling d1 = conv2d(d0, self.gf) d2 = conv2d(d1, self.gf*2) d3 = conv2d(d2, self.gf*4) d4 = conv2d(d3, self.gf*8) # Upsampling u1 = deconv2d(d4, d3, self.gf*4) u2 = deconv2d(u1, d2, self.gf*2) u3 = deconv2d(u2, d1, self.gf) u4 = UpSampling2D(size=2)(u3) output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4) return Model(d0, output_img)
Example 23
Project: Keras-GAN Author: eriklindernoren File: dcgan.py License: MIT License | 5 votes |
def build_discriminator(self): model = Sequential() model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(256, kernel_size=3, strides=1, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example 24
Project: tartarus Author: sergiooramas File: models.py License: MIT License | 5 votes |
def get_model_3(params): # metadata inputs2 = Input(shape=(params["n_metafeatures"],)) x2 = Dropout(params["dropout_factor"])(inputs2) if params["n_dense"] > 0: dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu') x2 = dense2(x2) logging.debug("Output CNN: %s" % str(dense2.output_shape)) x2 = Dropout(params["dropout_factor"])(x2) if params["n_dense_2"] > 0: dense3 = Dense(output_dim=params["n_dense_2"], init="uniform", activation='relu') x2 = dense3(x2) logging.debug("Output CNN: %s" % str(dense3.output_shape)) x2 = Dropout(params["dropout_factor"])(x2) dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation']) xout = dense4(x2) logging.debug("Output CNN: %s" % str(dense4.output_shape)) if params['final_activation'] == 'linear': reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) xout = reg(xout) model = Model(input=inputs2, output=xout) return model # Metadata 2 inputs, post-merge with dense layers
Example 25
Project: tartarus Author: sergiooramas File: models.py License: MIT License | 5 votes |
def get_model_32(params): # metadata inputs = Input(shape=(params["n_metafeatures"],)) reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) x1 = reg(inputs) inputs2 = Input(shape=(params["n_metafeatures2"],)) reg2 = Lambda(lambda x :K.l2_normalize(x, axis=1)) x2 = reg2(inputs2) # merge x = merge([x1, x2], mode='concat', concat_axis=1) x = Dropout(params["dropout_factor"])(x) if params['n_dense'] > 0: dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu') x = dense2(x) logging.debug("Output CNN: %s" % str(dense2.output_shape)) dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation']) xout = dense4(x) logging.debug("Output CNN: %s" % str(dense4.output_shape)) if params['final_activation'] == 'linear': reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) xout = reg(xout) model = Model(input=[inputs,inputs2], output=xout) return model # Metadata 3 inputs, pre-merge and l2
Example 26
Project: tartarus Author: sergiooramas File: models.py License: MIT License | 5 votes |
def get_model_33(params): # metadata inputs = Input(shape=(params["n_metafeatures"],)) reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) x1 = reg(inputs) inputs2 = Input(shape=(params["n_metafeatures2"],)) reg2 = Lambda(lambda x :K.l2_normalize(x, axis=1)) x2 = reg2(inputs2) inputs3 = Input(shape=(params["n_metafeatures3"],)) reg3 = Lambda(lambda x :K.l2_normalize(x, axis=1)) x3 = reg3(inputs3) # merge x = merge([x1, x2, x3], mode='concat', concat_axis=1) x = Dropout(params["dropout_factor"])(x) if params['n_dense'] > 0: dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu') x = dense2(x) logging.debug("Output CNN: %s" % str(dense2.output_shape)) dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation']) xout = dense4(x) logging.debug("Output CNN: %s" % str(dense4.output_shape)) if params['final_activation'] == 'linear': reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) xout = reg(xout) model = Model(input=[inputs,inputs2,inputs3], output=xout) return model # Metadata 4 inputs, pre-merge and l2
Example 27
Project: tartarus Author: sergiooramas File: models.py License: MIT License | 5 votes |
def get_model_34(params): # metadata inputs = Input(shape=(params["n_metafeatures"],)) reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) x1 = reg(inputs) inputs2 = Input(shape=(params["n_metafeatures2"],)) reg2 = Lambda(lambda x :K.l2_normalize(x, axis=1)) x2 = reg2(inputs2) inputs3 = Input(shape=(params["n_metafeatures3"],)) reg3 = Lambda(lambda x :K.l2_normalize(x, axis=1)) x3 = reg3(inputs3) inputs4 = Input(shape=(params["n_metafeatures4"],)) reg4 = Lambda(lambda x :K.l2_normalize(x, axis=1)) x4 = reg4(inputs4) # merge x = merge([x1, x2, x3, x4], mode='concat', concat_axis=1) x = Dropout(params["dropout_factor"])(x) if params['n_dense'] > 0: dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu') x = dense2(x) logging.debug("Output CNN: %s" % str(dense2.output_shape)) dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation']) xout = dense4(x) logging.debug("Output CNN: %s" % str(dense4.output_shape)) if params['final_activation'] == 'linear': reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) xout = reg(xout) model = Model(input=[inputs,inputs2,inputs3,inputs4], output=xout) return model
Example 28
Project: tartarus Author: sergiooramas File: models.py License: MIT License | 5 votes |
def get_model_6(params): # metadata inputs2 = Input(shape=(params["n_metafeatures"],)) #x2 = Dropout(params["dropout_factor"])(inputs2) if params["n_dense"] > 0: dense21 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu') x21 = dense21(inputs2) logging.debug("Output CNN: %s" % str(dense21.output_shape)) dense22 = Dense(output_dim=params["n_dense"], init="uniform", activation='tanh') x22 = dense22(inputs2) logging.debug("Output CNN: %s" % str(dense22.output_shape)) dense23 = Dense(output_dim=params["n_dense"], init="uniform", activation='sigmoid') x23 = dense23(inputs2) logging.debug("Output CNN: %s" % str(dense23.output_shape)) # merge x = merge([x21, x22, x23], mode='concat', concat_axis=1) x2 = Dropout(params["dropout_factor"])(x) if params["n_dense_2"] > 0: dense3 = Dense(output_dim=params["n_dense_2"], init="uniform", activation='relu') x2 = dense3(x2) logging.debug("Output CNN: %s" % str(dense3.output_shape)) x2 = Dropout(params["dropout_factor"])(x2) dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation']) xout = dense4(x2) logging.debug("Output CNN: %s" % str(dense4.output_shape)) if params['final_activation'] == 'linear': reg = Lambda(lambda x :K.l2_normalize(x, axis=1)) xout = reg(xout) model = Model(input=inputs2, output=xout) return model
Example 29
Project: View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition Author: microsoft File: va-rnn.py License: MIT License | 5 votes |
def creat_model(input_shape, num_class): init = initializers.Orthogonal(gain=args.norm) sequence_input =Input(shape=input_shape) mask = Masking(mask_value=0.)(sequence_input) if args.aug: mask = augmentaion()(mask) X = Noise(0.075)(mask) if args.model[0:2]=='VA': # VA trans = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X) trans = Dropout(0.5)(trans) trans = TimeDistributed(Dense(3,kernel_initializer='zeros'))(trans) rot = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X) rot = Dropout(0.5)(rot) rot = TimeDistributed(Dense(3,kernel_initializer='zeros'))(rot) transform = Concatenate()([rot,trans]) X = VA()([mask,transform]) X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X) X = Dropout(0.5)(X) X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X) X = Dropout(0.5)(X) X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X) X = Dropout(0.5)(X) X = TimeDistributed(Dense(num_class))(X) X = MeanOverTime()(X) X = Activation('softmax')(X) model=Model(sequence_input,X) return model
Example 30
Project: TaiwanTrainVerificationCode2text Author: linsamtw File: load_model.py License: Apache License 2.0 | 5 votes |
def load_model(): from keras.models import Model from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D tensor_in = Input((60, 200, 3)) out = tensor_in out = Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(out) out = Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(out) out = MaxPooling2D(pool_size=(2, 2))(out) out = Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')(out) out = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(out) out = MaxPooling2D(pool_size=(2, 2))(out) out = Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu')(out) out = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(out) out = MaxPooling2D(pool_size=(2, 2))(out) out = Conv2D(filters=256, kernel_size=(3, 3), activation='relu')(out) out = MaxPooling2D(pool_size=(2, 2))(out) out = Flatten()(out) out = Dropout(0.5)(out) out = [Dense(37, name='digit1', activation='softmax')(out),\ Dense(37, name='digit2', activation='softmax')(out),\ Dense(37, name='digit3', activation='softmax')(out),\ Dense(37, name='digit4', activation='softmax')(out),\ Dense(37, name='digit5', activation='softmax')(out),\ Dense(37, name='digit6', activation='softmax')(out)] model = Model(inputs=tensor_in, outputs=out) # Define the optimizer model.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy']) if 'Windows' in platform.platform(): model.load_weights('{}\\cnn_weight\\verificatioin_code.h5'.format(PATH)) else: model.load_weights('{}/cnn_weight/verificatioin_code.h5'.format(PATH)) return model