CVxTz

def get_model():
    nclass = len(list_labels)
    inp = Input(shape=(input_length, 1))
    img_1 = Convolution1D(16, kernel_size=9, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=9, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=16)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=4)(img_1)

CVxTz

def get_model_mel():

    nclass = len(list_labels)

    inp = Input(shape=(157, 320, 1))

    incep_res = InceptionResNetV2(input_shape=(157, 320, 1), weights=None, include_top=False)

    x = incep_res(inp)

CVxTz

def get_model(n_classes=1):

    base_model = ResNet50(weights='imagenet', include_top=False)

    #for layer in base_model.layers:
    #    layer.trainable = False

    x = base_model.output
    x = GlobalMaxPooling2D()(x)
    x = Dropout(0.5)(x)

CVxTz

def get_model_1(max_work, max_user):
    dim_embedddings = 30
    bias = 3
    # inputs
    w_inputs = Input(shape=(1,), dtype='int32')
    w = Embedding(max_work+1, dim_embedddings, name="work")(w_inputs)

    # context
    u_inputs = Input(shape=(1,), dtype='int32')
    u = Embedding(max_user+1, dim_embedddings, name="user")(u_inputs)

CVxTz

def get_model():
    nclass = 5
    inp = Input(shape=(187, 1))
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)

CVxTz

def get_unet(do=0, activation=ReLU):
    inputs = Input(input_shape+(3,))
    conv1 = Dropout(do)(activation()(Conv2D(32, (3, 3), padding='same')(inputs)))
    conv1 = Dropout(do)(activation()(Conv2D(32, (3, 3), padding='same')(conv1)))
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Dropout(do)(activation()(Conv2D(64, (3, 3), padding='same')(pool1)))
    conv2 = Dropout(do)(activation()(Conv2D(64, (3, 3), padding='same')(conv2)))
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

CVxTz

def get_base_model():
    inp = Input(shape=(WINDOW_SIZE*30, 1))
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)

CVxTz

def get_model_cnn():
    nclass = 5

    seq_input = Input(shape=(None, WINDOW_SIZE*30, 1))
    base_model = get_base_model()
    # for layer in base_model.layers:
    #     layer.trainable = False
    encoded_sequence = TimeDistributed(base_model)(seq_input)
    encoded_sequence = SpatialDropout1D(rate=0.01)(Convolution1D(128,
                                                               kernel_size=3,

CVxTz

def get_features_only_model(n_features, n_classes):
    in_ = Input((n_features,))
    x = Dense(10, activation="relu", kernel_regularizer=l1(0.001))(in_)
    x = Dropout(0.5)(x)
    x = Dense(n_classes, activation="softmax")(x)

    model = Model(in_, x)

    model.compile(loss="sparse_categorical_crossentropy", metrics=['acc'], optimizer="adam")

CVxTz

def get_graph_embedding_model(n_nodes):
    in_1 = Input((1,))
    in_2 = Input((1,))
    emb = Embedding(n_nodes, 100, name="node1")
    x1 = emb(in_1)
    x2 = emb(in_2)
    x1 = Flatten()(x1)
    x1 = Dropout(0.1)(x1)
    x2 = Flatten()(x2)
    x2 = Dropout(0.1)(x2)