deeperunderstanding

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

deeperunderstanding

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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batches = 10000
batch_size=64

losses_disc = []
losses_disc_cat = []
losses_ae = []
losses_val = []

real = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))

deeperunderstanding

FROM jupyter/scipy-notebook

RUN conda install --quiet --yes \
    'mlflow=1.0.0' \
    'psycopg2'

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

deeperunderstanding

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

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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 pip install psycopg2

EXPOSE 5000

RUN mkdir -p /server
WORKDIR /server
COPY . /server

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

deeperunderstanding