from typing import Tuple

from keras import Model, Sequential
from keras.layers import Embedding, Input, Conv1D, Dropout, TimeDistributed, Dense, PReLU
from keras_contrib.layers import CRF


class CNNModel(Model):
    def __init__(self,
                 nb_symbols: int = 34,
                 embeddings_size: int = 8,
                 kernel_sizes: Tuple[int, ...] = (5, 5, 5),
                 nb_filters: Tuple[int, ...] = (192, 192, 192),
                 dilations: Tuple[int, ...] = (1, 1, 1),
                 dense_output_units: int = 0,
                 dropout: float = 0.,
                 use_crf: bool = True,
                 nb_classes: int = 15,
                 inputs=None, outputs=None, name='SegmentationNetwork'):
        if inputs or outputs:
            super(CNNModel, self).__init__(inputs=inputs, outputs=outputs, name=name)
            return

        net_input = Input(shape=(None,), dtype='uint8', name='input')
        char_embeddings = Embedding(nb_symbols, embeddings_size, name='char_embeddings')(net_input)

        x = char_embeddings
        for kernel_size, filters, dilation in zip(kernel_sizes, nb_filters, dilations):
            x = Conv1D(filters, kernel_size, padding='same', dilation_rate=dilation)(x)
            x = PReLU(shared_axes=-1)(x)
            x = Dropout(dropout)(x)

        if dense_output_units:
            x = TimeDistributed(Sequential([
                Dense(dense_output_units),
                PReLU(),
            ]), name='pre_output')(x)

        output = CRF(units=nb_classes, learn_mode='join', name='output')(x) if use_crf else \
            TimeDistributed(Dense(nb_classes, activation='softmax', name='output'))(x)
        super(CNNModel, self).__init__(inputs=[net_input], outputs=[output], name=name)

d9651ed0efba7319608b7755b0d07fe3dd7458ef
<mask token>

<mask token>


class CNNModel(Model):
    <mask token>

<mask token>


class CNNModel(Model):

    def __init__(self, nb_symbols: int=34, embeddings_size: int=8,
        kernel_sizes: Tuple[int, ...]=(5, 5, 5), nb_filters: Tuple[int, ...
        ]=(192, 192, 192), dilations: Tuple[int, ...]=(1, 1, 1),
        dense_output_units: int=0, dropout: float=0.0, use_crf: bool=True,
        nb_classes: int=15, inputs=None, outputs=None, name=
        'SegmentationNetwork'):
        if inputs or outputs:
            super(CNNModel, self).__init__(inputs=inputs, outputs=outputs,
                name=name)
            return
        net_input = Input(shape=(None,), dtype='uint8', name='input')
        char_embeddings = Embedding(nb_symbols, embeddings_size, name=
            'char_embeddings')(net_input)
        x = char_embeddings
        for kernel_size, filters, dilation in zip(kernel_sizes, nb_filters,
            dilations):
            x = Conv1D(filters, kernel_size, padding='same', dilation_rate=
                dilation)(x)
            x = PReLU(shared_axes=-1)(x)
            x = Dropout(dropout)(x)
        if dense_output_units:
            x = TimeDistributed(Sequential([Dense(dense_output_units),
                PReLU()]), name='pre_output')(x)
        output = CRF(units=nb_classes, learn_mode='join', name='output')(x
            ) if use_crf else TimeDistributed(Dense(nb_classes, activation=
            'softmax', name='output'))(x)
        super(CNNModel, self).__init__(inputs=[net_input], outputs=[output],
            name=name)

from typing import Tuple
from keras import Model, Sequential
from keras.layers import Embedding, Input, Conv1D, Dropout, TimeDistributed, Dense, PReLU
from keras_contrib.layers import CRF


class CNNModel(Model):

    def __init__(self, nb_symbols: int=34, embeddings_size: int=8,
        kernel_sizes: Tuple[int, ...]=(5, 5, 5), nb_filters: Tuple[int, ...
        ]=(192, 192, 192), dilations: Tuple[int, ...]=(1, 1, 1),
        dense_output_units: int=0, dropout: float=0.0, use_crf: bool=True,
        nb_classes: int=15, inputs=None, outputs=None, name=
        'SegmentationNetwork'):
        if inputs or outputs:
            super(CNNModel, self).__init__(inputs=inputs, outputs=outputs,
                name=name)
            return
        net_input = Input(shape=(None,), dtype='uint8', name='input')
        char_embeddings = Embedding(nb_symbols, embeddings_size, name=
            'char_embeddings')(net_input)
        x = char_embeddings
        for kernel_size, filters, dilation in zip(kernel_sizes, nb_filters,
            dilations):
            x = Conv1D(filters, kernel_size, padding='same', dilation_rate=
                dilation)(x)
            x = PReLU(shared_axes=-1)(x)
            x = Dropout(dropout)(x)
        if dense_output_units:
            x = TimeDistributed(Sequential([Dense(dense_output_units),
                PReLU()]), name='pre_output')(x)
        output = CRF(units=nb_classes, learn_mode='join', name='output')(x
            ) if use_crf else TimeDistributed(Dense(nb_classes, activation=
            'softmax', name='output'))(x)
        super(CNNModel, self).__init__(inputs=[net_input], outputs=[output],
            name=name)

from typing import Tuple

from keras import Model, Sequential
from keras.layers import Embedding, Input, Conv1D, Dropout, TimeDistributed, Dense, PReLU
from keras_contrib.layers import CRF


class CNNModel(Model):
    def __init__(self,
                 nb_symbols: int = 34,
                 embeddings_size: int = 8,
                 kernel_sizes: Tuple[int, ...] = (5, 5, 5),
                 nb_filters: Tuple[int, ...] = (192, 192, 192),
                 dilations: Tuple[int, ...] = (1, 1, 1),
                 dense_output_units: int = 0,
                 dropout: float = 0.,
                 use_crf: bool = True,
                 nb_classes: int = 15,
                 inputs=None, outputs=None, name='SegmentationNetwork'):
        if inputs or outputs:
            super(CNNModel, self).__init__(inputs=inputs, outputs=outputs, name=name)
            return

        net_input = Input(shape=(None,), dtype='uint8', name='input')
        char_embeddings = Embedding(nb_symbols, embeddings_size, name='char_embeddings')(net_input)

        x = char_embeddings
        for kernel_size, filters, dilation in zip(kernel_sizes, nb_filters, dilations):
            x = Conv1D(filters, kernel_size, padding='same', dilation_rate=dilation)(x)
            x = PReLU(shared_axes=-1)(x)
            x = Dropout(dropout)(x)

        if dense_output_units:
            x = TimeDistributed(Sequential([
                Dense(dense_output_units),
                PReLU(),
            ]), name='pre_output')(x)

        output = CRF(units=nb_classes, learn_mode='join', name='output')(x) if use_crf else \
            TimeDistributed(Dense(nb_classes, activation='softmax', name='output'))(x)
        super(CNNModel, self).__init__(inputs=[net_input], outputs=[output], name=name)
