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window_size = train_x.shape[1]
input_dim = train_x.shape[2]
latent_dim = 32
cat_dim = 8

prior_discriminator = create_discriminator(latent_dim)
prior_discriminator.compile(loss='binary_crossentropy', 
                            optimizer=Nadam(0.0002, 0.5), 
                            metrics=['accuracy'])

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def create_discriminator(latent_dim):
    input_layer = Input(shape=(latent_dim,))
    disc = Dense(128)(input_layer)
    disc = ELU()(disc)
    disc = Dense(64)(disc)
    disc = ELU()(disc)
    disc = Dense(1, activation="sigmoid")(disc)
    
    model = Model(input_layer, disc)
    return model

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def create_encoder(latent_dim, cat_dim, window_size, input_dim):
    input_layer = Input(shape=(window_size, input_dim))
    
    code = TimeDistributed(Dense(64, activation='linear'))(input_layer)
    code = Bidirectional(LSTM(128, return_sequences=True))(code)
    code = BatchNormalization()(code)
    code = ELU()(code)
    code = Bidirectional(LSTM(64))(code)
    code = BatchNormalization()(code)
    code = ELU()(code)

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version: '3'
services:
  notebook:
    build:
      context: ./jupyter-notebook-docker
    ports:
      - "8888:8888"
    depends_on: 
      - mlflow
    environment: 

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CREATE DATABASE mlflow;

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FROM postgres:alpine

COPY init.sql /docker-entrypoint-initdb.d/

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FROM python:3.7.0

RUN pip install mlflow==1.0.0
RUN pip install psycopg2

RUN mkdir /mlflow/

CMD mlflow server \
    --backend-store-uri postgresql://admin:secret@postgres:5432/mlflow \
    --default-artifact-root /mlflow \

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FROM python:3.7.0

RUN pip install mlflow==1.0.0

RUN mkdir /mlflow/

CMD mlflow server \
    --backend-store-uri /mlflow \
    --host 0.0.0.0