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# Install tensorflow for macOS from https://github.com/apple/tensorflow_macos

import argparse
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from tensorflow.python.compiler.mlcompute import mlcompute


def get_dataset(hparams):
  (train_images, train_labels), (test_images,

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Table of content

• School of Informatics OpenVPN
• SSH Config File
• Understanding the DICE file system
• Install conda
• sshfs: mount system as network drive
• Copy large files and folders

School of Informatics OpenVPN

It is much simpler and faster to access the various school's servers when connected to the School of Informatics OpenVPN.

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def evaluate(sentence):
  sentence = preprocess_sentence(sentence)

  sentence = tf.expand_dims(
      START_TOKEN + tokenizer.encode(sentence) + END_TOKEN, axis=0)

  output = tf.expand_dims(START_TOKEN, 0)

  for i in range(MAX_LENGTH):
    predictions = model(inputs=[sentence, output], training=False)

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def loss_function(y_true, y_pred):
  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))
  
  loss = tf.keras.losses.SparseCategoricalCrossentropy(
      from_logits=True, reduction='none')(y_true, y_pred)

  mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)
  loss = tf.multiply(loss, mask)

  return tf.reduce_mean(loss)

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NUM_LAYERS = 2
D_MODEL = 256
NUM_HEADS = 8
UNITS = 512
DROPOUT = 0.1

model = transformer(
    vocab_size=VOCAB_SIZE,
    num_layers=NUM_LAYERS,
    units=UNITS,

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def transformer(vocab_size,
                num_layers,
                units,
                d_model,
                num_heads,
                dropout,
                name="transformer"):
  inputs = tf.keras.Input(shape=(None,), name="inputs")
  dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")

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def decoder(vocab_size,
            num_layers,
            units,
            d_model,
            num_heads,
            dropout,
            name='decoder'):
  inputs = tf.keras.Input(shape=(None,), name='inputs')
  enc_outputs = tf.keras.Input(shape=(None, d_model), name='encoder_outputs')
  look_ahead_mask = tf.keras.Input(

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def decoder_layer(units, d_model, num_heads, dropout, name="decoder_layer"):
  inputs = tf.keras.Input(shape=(None, d_model), name="inputs")
  enc_outputs = tf.keras.Input(shape=(None, d_model), name="encoder_outputs")
  look_ahead_mask = tf.keras.Input(
      shape=(1, None, None), name="look_ahead_mask")
  padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')

  attention1 = MultiHeadAttention(
      d_model, num_heads, name="attention_1")(inputs={
          'query': inputs,

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def encoder(vocab_size,
            num_layers,
            units,
            d_model,
            num_heads,
            dropout,
            name="encoder"):
  inputs = tf.keras.Input(shape=(None,), name="inputs")
  padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")