from tensorflow.keras.layers import Input, Embedding, Conv1D, GlobalMaxPooling1D, Dense

from tensorflow.keras.models import Model

from tensorflow.keras.optimizers import Adam

# hyperparameter

vocabulary_size = 10,000

max_sequence_length = 100

embedding_size = 100

num_filters = 128

kernel_size = 3

hidden_units = 64

num_classes = 2

input_text = Input(shape=(max_sequence_length,), dtype=‘int32’)

embedding=Embedding(input_dim=vocabulary_size, output_dim=embedding_size, input_length=max_sequence_length)(input_text)

convolution=Conv1D(filters=num_filters,kernel_size=kernel_size,activation=‘relu’)(embedding)

pooling = GlobalMaxPooling1D()(convolution)

dense = Dense(hidden_units, activation=‘relu’)(pooling)

output = Dense(num_classes, activation=‘softmax’)(dense)

model = Model(inputs=input_text, outputs=output)

model.compile(loss=‘categorical_crossentropy’, optimizer=Adam(), metrics=[‘accuracy’])