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def validate_csv(text, field_count=4):
    # Quick validation check to make sure input is sane 
    return len(text.split(", ")) == field_count
    
def generate_rec(model, start_string, num_chars, temperature, expected_field_count):
    invalid_record_count = 0
    valid_record_count = 0
    
    # Convert our start string to numbers (vectorizing)
    input_eval = [char2idx[s] for s in start_string]

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# Define loss function
def loss(labels, logits):
  return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)

# Compile model
model.compile(optimizer='adam', loss=loss)

# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'

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def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
    dropout_rate = 0.2
    rnn_initializer = 'glorot_uniform'

    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(vocab_size, embedding_dim,
                                  batch_input_shape=[batch_size, None]),
        tf.keras.layers.Dropout(dropout_rate),
        tf.keras.layers.LSTM(rnn_units,
                             return_sequences=True,

zredlined

def split_input_target(chunk):
    input_text = chunk[:-1]
    target_text = chunk[1:]
    return input_text, target_text

BATCH_SIZE = 64
BUFFER_SIZE = 10000

# Create TensorFlow training examples and targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)

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# Count the unique characters in the file
vocab = sorted(set(text))

# Creating a mapping from unique characters to indices
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text])

print (f'Training set contains {len(vocab)} unique characters')
print (f'First 20 characters -> index mappings')

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{
    "bike_id": "0cc988be-638e-4c4d-9e83-da461db37d35",
    "epoch": 1579837235,
    "is_disabled": 0,
    "is_reserved": 0,
    "jump_ebike_battery_level": "90%",
    "jump_vehicle_name": "EVT822",
    "jump_vehicle_type": "scooter",
    "lat": 34.035088,
    "locations": [

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// Pointcloud
pointGeometry = new THREE.BufferGeometry();
var pointArray = [];
var colorArray = [];

for (i = 0; i < points_dataset.length; i++) {

        // Space apart ARKit points appropriately into new environment
        var x = points_dataset[i]["x"] * POINT_SPACING;
        var y = points_dataset[i]["y"] * POINT_SPACING;

zredlined

<!-- D3js imports to load CSV data -->
<script src="https://d3js.org/d3-collection.v1.min.js"></script>
<script src="https://d3js.org/d3-dispatch.v1.min.js"></script>
<script src="https://d3js.org/d3-dsv.v1.min.js"></script>
<script src="https://d3js.org/d3-request.v1.min.js"></script>
<script src="https://d3js.org/d3-queue.v3.min.js"></script>

<!-- load multiple data asynchronously, defer visualization until complete -->
d3.queue()
.defer(d3.csv, "./data/spotmeka_points.csv")