自学教程：Python layers.Dense方法代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]def _save(model, base_model, layers, labels, random_seed, checkpoints_dir):    from keras.layers import Flatten, Dense    from keras import Model    nclasses = len(labels)    x = Flatten()(base_model.output)    x = _makenet(x, layers, dropout=None, random_seed=random_seed)    predictions = Dense(nclasses, activation="softmax", name="predictions")(x)    model_final = Model(inputs=base_model.input, outputs=predictions)    for i in range(layers - 1):        weights = model.get_layer(name='dense_layer_{}'.format(i)).get_weights()        model_final.get_layer(name='dense_layer_{}'.format(i)).set_weights(weights)    weights = model.get_layer(name='predictions').get_weights()    model_final.get_layer(name='predictions').set_weights(weights)    model_final.save(os.path.join(checkpoints_dir, "model.h5"))    with open(os.path.join(checkpoints_dir, "labels.txt"), "w") as f:        f.write("/n".join(labels))    return model_final 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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]) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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]) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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)) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dense [as 别名]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))