securetorobert

NCLASSES = 2
HEIGHT = 224
WIDTH = 224
NUM_CHANNELS = 3
BATCH_SIZE = 32

securetorobert

!pip install tensorflow-gpu==2.0.0-beta0

import tensorflow as tf
from tensorflow.keras import layers, models, optimizers

securetorobert

# Use an official Python runtime as a parent image
FROM python:2.7-slim

# Set the working directory to /app
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . /app

# Install any needed packages specified in requirements.txt

securetorobert

#standardSQL
SELECT
  *
FROM
  ML.PREDICT(MODEL `bqml_tutorial.sample_model`, (
SELECT
  IFNULL(device.operatingSystem, "") AS os,
  device.isMobile AS is_mobile,
  IFNULL(totals.pageviews, 0) AS pageviews,
  IFNULL(geoNetwork.country, "") AS country

securetorobert

SELECT
  *
FROM
  ML.CONFUSION_MATRIX(MODEL `bqml_tutorial.sample_model`,
    (
    SELECT
      IF(totals.transactions IS NULL, 0, 1) AS label,
      IFNULL(device.operatingSystem, "") AS os,
      device.isMobile AS is_mobile,
      IFNULL(geoNetwork.country, "") AS country,

securetorobert

SELECT
  *
FROM
  ML.ROC_CURVE(MODEL `bqml_tutorial.sample_model`,
    (
    SELECT
      IF(totals.transactions IS NULL, 0, 1) AS label,
      IFNULL(device.operatingSystem, "") AS os,
      device.isMobile AS is_mobile,
      IFNULL(geoNetwork.country, "") AS country,

securetorobert

SELECT
  *
FROM
  ML.EVALUATE(MODEL `bqml_tutorial.sample_model`,
    (
    SELECT
      IF(totals.transactions IS NULL, 0, 1) AS label,
      IFNULL(device.operatingSystem, "") AS os,
      device.isMobile AS is_mobile,
      IFNULL(geoNetwork.country, "") AS country,

securetorobert

#standardSQL
CREATE MODEL `bqml_tutorial.sample_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
  IF(totals.transactions IS NULL, 0, 1) AS label,
  IFNULL(device.operatingSystem, "") AS os,
  device.isMobile AS is_mobile,
  IFNULL(geoNetwork.country, "") AS country,
  IFNULL(totals.pageviews, 0) AS pageviews
FROM

securetorobert

AUTOTUNE = tf.data.experimental.AUTOTUNE

path_ds = tf.data.Dataset.from_tensor_slices(train_file_list)
image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(train_label_list, tf.int64))
image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))

ds = image_label_ds.shuffle(buffer_size=1000 * BATCH_SIZE)
ds = ds.repeat()
ds = ds.batch(BATCH_SIZE)

securetorobert

import tensorflow as tf
from tensorflow import keras

model = keras.models.Sequential([
  keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(4,), kernel_regularizer=keras.regularizers.l1_l2(l1=0.1, l2=0.01)),
  keras.layers.Dense(4, activation=tf.nn.relu, kernel_regularizer=keras.regularizers.l1_l2(l1=0.1, l2=0.01)),
  keras.layers.Dense(1, activation=tf.nn.sigmoid)
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])