2b. Machine Learning using tf.estimator

In this notebook, we will create a machine learning model using tf.estimator and evaluate its performance. The dataset is rather small (7700 samples), so we can do it all in-memory. We will also simply pass the raw data in as-is.

In [1]:
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil

print(tf.__version__)
1.13.1

Read data created in the previous chapter.

In [2]:
# In CSV, label is the first column, after the features, followed by the key
CSV_COLUMNS = ['fare_amount', 'pickuplon','pickuplat','dropofflon','dropofflat','passengers', 'key']
FEATURES = CSV_COLUMNS[1:len(CSV_COLUMNS) - 1]
LABEL = CSV_COLUMNS[0]

df_train = pd.read_csv('./taxi-train.csv', header = None, names = CSV_COLUMNS)
df_valid = pd.read_csv('./taxi-valid.csv', header = None, names = CSV_COLUMNS)
df_test = pd.read_csv('./taxi-test.csv', header = None, names = CSV_COLUMNS)

Train and eval input functions to read from Pandas Dataframe

In [3]:
def make_train_input_fn(df, num_epochs):
  return tf.estimator.inputs.pandas_input_fn(
    x = df,
    y = df[LABEL],
    batch_size = 128,
    num_epochs = num_epochs,
    shuffle = True,
    queue_capacity = 1000
  )
In [4]:
def make_eval_input_fn(df):
  return tf.estimator.inputs.pandas_input_fn(
    x = df,
    y = df[LABEL],
    batch_size = 128,
    shuffle = False,
    queue_capacity = 1000
  )

Our input function for predictions is the same except we don't provide a label

In [5]:
def make_prediction_input_fn(df):
  return tf.estimator.inputs.pandas_input_fn(
    x = df,
    y = None,
    batch_size = 128,
    shuffle = False,
    queue_capacity = 1000
  )

Create feature columns for estimator

In [6]:
def make_feature_cols():
  input_columns = [tf.feature_column.numeric_column(k) for k in FEATURES]
  return input_columns

Linear Regression with tf.Estimator framework

In [7]:
tf.logging.set_verbosity(tf.logging.INFO)

OUTDIR = 'taxi_trained'
shutil.rmtree(OUTDIR, ignore_errors = True) # start fresh each time

model = tf.estimator.LinearRegressor(
      feature_columns = make_feature_cols(), model_dir = OUTDIR)

model.train(input_fn = make_train_input_fn(df_train, num_epochs = 10))
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_experimental_distribute': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_global_id_in_cluster': 0, '_tf_random_seed': None, '_service': None, '_num_ps_replicas': 0, '_evaluation_master': '', '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_task_id': 0, '_device_fn': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f1621c9b240>, '_eval_distribute': None, '_keep_checkpoint_max': 5, '_master': '', '_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100, '_protocol': None, '_task_type': 'worker', '_is_chief': True, '_save_checkpoints_secs': 600, '_model_dir': 'taxi_trained', '_num_worker_replicas': 1, '_train_distribute': None}
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2703: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py:809: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Saving checkpoints for 0 into taxi_trained/model.ckpt.
INFO:tensorflow:loss = 24785.195, step = 1
INFO:tensorflow:global_step/sec: 162.472
INFO:tensorflow:loss = 10496.74, step = 101 (0.621 sec)
INFO:tensorflow:global_step/sec: 271.305
INFO:tensorflow:loss = 11549.643, step = 201 (0.367 sec)
INFO:tensorflow:global_step/sec: 290.197
INFO:tensorflow:loss = 21385.969, step = 301 (0.344 sec)
INFO:tensorflow:global_step/sec: 285.312
INFO:tensorflow:loss = 12134.15, step = 401 (0.351 sec)
INFO:tensorflow:global_step/sec: 282.254
INFO:tensorflow:loss = 10723.378, step = 501 (0.355 sec)
INFO:tensorflow:global_step/sec: 238.179
INFO:tensorflow:loss = 15577.61, step = 601 (0.417 sec)
INFO:tensorflow:Saving checkpoints for 608 into taxi_trained/model.ckpt.
INFO:tensorflow:Loss for final step: 143.04446.
Out[7]:
<tensorflow_estimator.python.estimator.canned.linear.LinearRegressor at 0x7f15f3efff98>

Evaluate on the validation data (we should defer using the test data to after we have selected a final model).

In [8]:
def print_rmse(model, df):
  metrics = model.evaluate(input_fn = make_eval_input_fn(df))
  print('RMSE on dataset = {}'.format(np.sqrt(metrics['average_loss'])))
print_rmse(model, df_valid)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2019-07-01T20:21:23Z
INFO:tensorflow:Graph was finalized.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from taxi_trained/model.ckpt-608
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2019-07-01-20:21:23
INFO:tensorflow:Saving dict for global step 608: average_loss = 109.50432, global_step = 608, label/mean = 11.666427, loss = 13023.192, prediction/mean = 10.842143
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 608: taxi_trained/model.ckpt-608
RMSE on dataset = 10.464430809020996

This is nowhere near our benchmark (RMSE of $6 or so on this data), but it serves to demonstrate what TensorFlow code looks like. Let's use this model for prediction.

In [9]:
predictions = model.predict(input_fn = make_prediction_input_fn(df_test))
for items in predictions:
  print(items)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from taxi_trained/model.ckpt-608
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
{'predictions': array([10.795108], dtype=float32)}
{'predictions': array([10.792586], dtype=float32)}
{'predictions': array([10.79344], dtype=float32)}
{'predictions': array([10.791017], dtype=float32)}
{'predictions': array([10.79485], dtype=float32)}
{'predictions': array([10.794632], dtype=float32)}
{'predictions': array([10.793227], dtype=float32)}
{'predictions': array([10.793259], dtype=float32)}
{'predictions': array([10.795003], dtype=float32)}
{'predictions': array([10.792877], dtype=float32)}
{'predictions': array([10.79511], dtype=float32)}
{'predictions': array([10.795273], dtype=float32)}
{'predictions': array([10.788619], dtype=float32)}
{'predictions': array([10.792499], dtype=float32)}
{'predictions': array([10.862182], dtype=float32)}
{'predictions': array([10.793302], dtype=float32)}
{'predictions': array([10.794137], dtype=float32)}
{'predictions': array([10.863196], dtype=float32)}
{'predictions': array([10.79446], dtype=float32)}
{'predictions': array([10.791657], dtype=float32)}
{'predictions': array([10.79289], dtype=float32)}
{'predictions': array([10.788843], dtype=float32)}
{'predictions': array([10.793038], dtype=float32)}
{'predictions': array([10.864346], dtype=float32)}
{'predictions': array([10.795702], dtype=float32)}
{'predictions': array([10.7856], dtype=float32)}
{'predictions': array([10.928023], dtype=float32)}
{'predictions': array([11.13833], dtype=float32)}
{'predictions': array([10.796178], dtype=float32)}
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{'predictions': array([10.93002], dtype=float32)}
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{'predictions': array([10.999011], dtype=float32)}
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{'predictions': array([10.796118], dtype=float32)}
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{'predictions': array([10.794223], dtype=float32)}
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{'predictions': array([10.793286], dtype=float32)}
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{'predictions': array([10.859996], dtype=float32)}
{'predictions': array([10.787695], dtype=float32)}
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{'predictions': array([10.793712], dtype=float32)}
{'predictions': array([11.067685], dtype=float32)}
{'predictions': array([10.793203], dtype=float32)}
{'predictions': array([10.800755], dtype=float32)}
{'predictions': array([10.792137], dtype=float32)}
{'predictions': array([10.862256], dtype=float32)}
{'predictions': array([10.790132], dtype=float32)}
{'predictions': array([10.790778], dtype=float32)}
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{'predictions': array([10.793741], dtype=float32)}
{'predictions': array([11.069238], dtype=float32)}
{'predictions': array([10.7909565], dtype=float32)}
{'predictions': array([10.931543], dtype=float32)}
{'predictions': array([10.794918], dtype=float32)}
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{'predictions': array([10.792982], dtype=float32)}
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{'predictions': array([10.794749], dtype=float32)}
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{'predictions': array([10.793889], dtype=float32)}
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{'predictions': array([10.930683], dtype=float32)}
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{'predictions': array([10.863866], dtype=float32)}
{'predictions': array([10.789664], dtype=float32)}
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{'predictions': array([10.795083], dtype=float32)}
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{'predictions': array([11.138756], dtype=float32)}
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{'predictions': array([10.930397], dtype=float32)}
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{'predictions': array([10.859096], dtype=float32)}
{'predictions': array([10.793628], dtype=float32)}
{'predictions': array([10.792626], dtype=float32)}
{'predictions': array([10.862816], dtype=float32)}
{'predictions': array([10.7961035], dtype=float32)}
{'predictions': array([10.931086], dtype=float32)}
{'predictions': array([10.792266], dtype=float32)}
{'predictions': array([10.794184], dtype=float32)}
{'predictions': array([10.861005], dtype=float32)}
{'predictions': array([10.793769], dtype=float32)}
{'predictions': array([10.793315], dtype=float32)}
{'predictions': array([10.794328], dtype=float32)}
{'predictions': array([11.0685835], dtype=float32)}
{'predictions': array([10.792565], dtype=float32)}
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{'predictions': array([10.861402], dtype=float32)}
{'predictions': array([10.794112], dtype=float32)}
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{'predictions': array([10.79328], dtype=float32)}
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{'predictions': array([10.790288], dtype=float32)}
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{'predictions': array([10.793069], dtype=float32)}
{'predictions': array([10.932459], dtype=float32)}
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{'predictions': array([10.792892], dtype=float32)}
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{'predictions': array([10.795641], dtype=float32)}
{'predictions': array([10.9332695], dtype=float32)}
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{'predictions': array([10.862181], dtype=float32)}
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{'predictions': array([10.795249], dtype=float32)}
{'predictions': array([11.136903], dtype=float32)}
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{'predictions': array([10.794021], dtype=float32)}
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{'predictions': array([11.139691], dtype=float32)}
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{'predictions': array([10.930896], dtype=float32)}
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{'predictions': array([11.001122], dtype=float32)}
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{'predictions': array([11.067121], dtype=float32)}
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{'predictions': array([10.9338665], dtype=float32)}
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{'predictions': array([11.000989], dtype=float32)}
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{'predictions': array([11.136204], dtype=float32)}
{'predictions': array([10.792781], dtype=float32)}
{'predictions': array([10.7935], dtype=float32)}
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{'predictions': array([10.862669], dtype=float32)}
{'predictions': array([10.796706], dtype=float32)}
{'predictions': array([10.864902], dtype=float32)}
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{'predictions': array([10.792827], dtype=float32)}
{'predictions': array([10.861414], dtype=float32)}
{'predictions': array([10.861724], dtype=float32)}
{'predictions': array([10.792628], dtype=float32)}
{'predictions': array([11.0687895], dtype=float32)}
{'predictions': array([11.069758], dtype=float32)}
{'predictions': array([10.931689], dtype=float32)}
{'predictions': array([10.787405], dtype=float32)}
{'predictions': array([10.794149], dtype=float32)}
{'predictions': array([11.069722], dtype=float32)}
{'predictions': array([10.862342], dtype=float32)}
{'predictions': array([10.794358], dtype=float32)}
{'predictions': array([10.793949], dtype=float32)}
{'predictions': array([10.933014], dtype=float32)}
{'predictions': array([10.794269], dtype=float32)}
{'predictions': array([10.793494], dtype=float32)}
{'predictions': array([10.800199], dtype=float32)}
{'predictions': array([10.793027], dtype=float32)}
{'predictions': array([10.792783], dtype=float32)}
{'predictions': array([11.063872], dtype=float32)}
{'predictions': array([10.785806], dtype=float32)}
{'predictions': array([10.793414], dtype=float32)}
{'predictions': array([11.068035], dtype=float32)}
{'predictions': array([10.793078], dtype=float32)}
{'predictions': array([10.793833], dtype=float32)}
{'predictions': array([10.858687], dtype=float32)}
{'predictions': array([10.787207], dtype=float32)}
{'predictions': array([10.793625], dtype=float32)}
{'predictions': array([10.861131], dtype=float32)}
{'predictions': array([10.79386], dtype=float32)}
{'predictions': array([11.064622], dtype=float32)}
{'predictions': array([10.794569], dtype=float32)}
{'predictions': array([10.9293165], dtype=float32)}
{'predictions': array([11.068538], dtype=float32)}
{'predictions': array([11.070415], dtype=float32)}
{'predictions': array([10.779492], dtype=float32)}
{'predictions': array([10.791143], dtype=float32)}
{'predictions': array([10.793862], dtype=float32)}
{'predictions': array([10.794976], dtype=float32)}
{'predictions': array([11.0675335], dtype=float32)}
{'predictions': array([10.794076], dtype=float32)}
{'predictions': array([10.790166], dtype=float32)}
{'predictions': array([10.79411], dtype=float32)}
{'predictions': array([10.794297], dtype=float32)}
{'predictions': array([11.000471], dtype=float32)}
{'predictions': array([10.793992], dtype=float32)}
{'predictions': array([10.863515], dtype=float32)}
{'predictions': array([10.859075], dtype=float32)}
{'predictions': array([10.793156], dtype=float32)}
{'predictions': array([10.79441], dtype=float32)}
{'predictions': array([10.793774], dtype=float32)}
{'predictions': array([10.862161], dtype=float32)}
{'predictions': array([10.79193], dtype=float32)}
{'predictions': array([10.79455], dtype=float32)}
{'predictions': array([11.136684], dtype=float32)}
{'predictions': array([10.79337], dtype=float32)}
{'predictions': array([10.792302], dtype=float32)}
{'predictions': array([10.792218], dtype=float32)}
{'predictions': array([11.137043], dtype=float32)}
{'predictions': array([10.79342], dtype=float32)}
{'predictions': array([10.853791], dtype=float32)}

This explains why the RMSE was so high -- the model essentially predicts the same amount for every trip. Would a more complex model help? Let's try using a deep neural network. The code to do this is quite straightforward as well.

Deep Neural Network regression

In [10]:
tf.logging.set_verbosity(tf.logging.INFO)
shutil.rmtree(OUTDIR, ignore_errors = True) # start fresh each time
model = tf.estimator.DNNRegressor(hidden_units = [32, 8, 2],
      feature_columns = make_feature_cols(), model_dir = OUTDIR)
model.train(input_fn = make_train_input_fn(df_train, num_epochs = 100));
print_rmse(model, df_valid)
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_experimental_distribute': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_global_id_in_cluster': 0, '_tf_random_seed': None, '_service': None, '_num_ps_replicas': 0, '_evaluation_master': '', '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_task_id': 0, '_device_fn': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f15d7d567f0>, '_eval_distribute': None, '_keep_checkpoint_max': 5, '_master': '', '_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100, '_protocol': None, '_task_type': 'worker', '_is_chief': True, '_save_checkpoints_secs': 600, '_model_dir': 'taxi_trained', '_num_worker_replicas': 1, '_train_distribute': None}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into taxi_trained/model.ckpt.
INFO:tensorflow:loss = 467570.2, step = 1
INFO:tensorflow:global_step/sec: 100.82
INFO:tensorflow:loss = 20871.418, step = 101 (0.994 sec)
INFO:tensorflow:global_step/sec: 108.186
INFO:tensorflow:loss = 19313.43, step = 201 (0.926 sec)
INFO:tensorflow:global_step/sec: 112.618
INFO:tensorflow:loss = 26053.674, step = 301 (0.890 sec)
INFO:tensorflow:global_step/sec: 94.7783
INFO:tensorflow:loss = 24630.527, step = 401 (1.054 sec)
INFO:tensorflow:global_step/sec: 123.391
INFO:tensorflow:loss = 20816.373, step = 501 (0.811 sec)
INFO:tensorflow:global_step/sec: 125.027
INFO:tensorflow:loss = 19305.867, step = 601 (0.800 sec)
INFO:tensorflow:global_step/sec: 100.336
INFO:tensorflow:loss = 17916.236, step = 701 (0.997 sec)
INFO:tensorflow:global_step/sec: 123.813
INFO:tensorflow:loss = 16324.613, step = 801 (0.808 sec)
INFO:tensorflow:global_step/sec: 96.5849
INFO:tensorflow:loss = 25742.564, step = 901 (1.034 sec)
INFO:tensorflow:global_step/sec: 126.786
INFO:tensorflow:loss = 20716.746, step = 1001 (0.789 sec)
INFO:tensorflow:global_step/sec: 102.184
INFO:tensorflow:loss = 17350.438, step = 1101 (0.979 sec)
INFO:tensorflow:global_step/sec: 129.286
INFO:tensorflow:loss = 23090.242, step = 1201 (0.773 sec)
INFO:tensorflow:global_step/sec: 119.124
INFO:tensorflow:loss = 14777.045, step = 1301 (0.840 sec)
INFO:tensorflow:global_step/sec: 96.1406
INFO:tensorflow:loss = 20134.01, step = 1401 (1.040 sec)
INFO:tensorflow:global_step/sec: 112.909
INFO:tensorflow:loss = 15141.786, step = 1501 (0.882 sec)
INFO:tensorflow:global_step/sec: 102.4
INFO:tensorflow:loss = 17473.344, step = 1601 (0.980 sec)
INFO:tensorflow:global_step/sec: 127.155
INFO:tensorflow:loss = 20703.318, step = 1701 (0.787 sec)
INFO:tensorflow:global_step/sec: 118.505
INFO:tensorflow:loss = 20141.992, step = 1801 (0.844 sec)
INFO:tensorflow:global_step/sec: 104.505
INFO:tensorflow:loss = 18941.66, step = 1901 (0.957 sec)
INFO:tensorflow:global_step/sec: 121.39
INFO:tensorflow:loss = 10826.924, step = 2001 (0.820 sec)
INFO:tensorflow:global_step/sec: 103.776
INFO:tensorflow:loss = 15104.154, step = 2101 (0.967 sec)
INFO:tensorflow:global_step/sec: 117.015
INFO:tensorflow:loss = 16825.898, step = 2201 (0.855 sec)
INFO:tensorflow:global_step/sec: 125.782
INFO:tensorflow:loss = 17469.46, step = 2301 (0.794 sec)
INFO:tensorflow:global_step/sec: 106.035
INFO:tensorflow:loss = 15310.082, step = 2401 (0.944 sec)
INFO:tensorflow:global_step/sec: 121.658
INFO:tensorflow:loss = 22304.357, step = 2501 (0.818 sec)
INFO:tensorflow:global_step/sec: 99.4088
INFO:tensorflow:loss = 19509.504, step = 2601 (1.006 sec)
INFO:tensorflow:global_step/sec: 114.029
INFO:tensorflow:loss = 24601.023, step = 2701 (0.877 sec)
INFO:tensorflow:global_step/sec: 55.0054
INFO:tensorflow:loss = 21849.145, step = 2801 (1.817 sec)
INFO:tensorflow:global_step/sec: 128.72
INFO:tensorflow:loss = 11309.27, step = 2901 (0.777 sec)
INFO:tensorflow:global_step/sec: 93.5308
INFO:tensorflow:loss = 21104.457, step = 3001 (1.069 sec)
INFO:tensorflow:global_step/sec: 96.7103
INFO:tensorflow:loss = 16993.004, step = 3101 (1.034 sec)
INFO:tensorflow:global_step/sec: 91.3205
INFO:tensorflow:loss = 18189.752, step = 3201 (1.098 sec)
INFO:tensorflow:global_step/sec: 112.614
INFO:tensorflow:loss = 14859.451, step = 3301 (0.888 sec)
INFO:tensorflow:global_step/sec: 90.5019
INFO:tensorflow:loss = 12333.189, step = 3401 (1.105 sec)
INFO:tensorflow:global_step/sec: 104.302
INFO:tensorflow:loss = 5995.971, step = 3501 (0.959 sec)
INFO:tensorflow:global_step/sec: 92.9583
INFO:tensorflow:loss = 10282.813, step = 3601 (1.075 sec)
INFO:tensorflow:global_step/sec: 104.575
INFO:tensorflow:loss = 9950.309, step = 3701 (0.957 sec)
INFO:tensorflow:global_step/sec: 88.9567
INFO:tensorflow:loss = 16426.35, step = 3801 (1.124 sec)
INFO:tensorflow:global_step/sec: 100.309
INFO:tensorflow:loss = 22024.291, step = 3901 (0.997 sec)
INFO:tensorflow:global_step/sec: 105.657
INFO:tensorflow:loss = 14616.185, step = 4001 (0.947 sec)
INFO:tensorflow:global_step/sec: 100.456
INFO:tensorflow:loss = 13213.275, step = 4101 (0.996 sec)
INFO:tensorflow:global_step/sec: 113.571
INFO:tensorflow:loss = 12385.185, step = 4201 (0.877 sec)
INFO:tensorflow:global_step/sec: 93.7462
INFO:tensorflow:loss = 9375.332, step = 4301 (1.067 sec)
INFO:tensorflow:global_step/sec: 112.179
INFO:tensorflow:loss = 14900.719, step = 4401 (0.894 sec)
INFO:tensorflow:global_step/sec: 93.2297
INFO:tensorflow:loss = 12472.631, step = 4501 (1.071 sec)
INFO:tensorflow:global_step/sec: 106.669
INFO:tensorflow:loss = 15485.025, step = 4601 (0.937 sec)
INFO:tensorflow:global_step/sec: 92.3307
INFO:tensorflow:loss = 18741.918, step = 4701 (1.083 sec)
INFO:tensorflow:global_step/sec: 106.823
INFO:tensorflow:loss = 13244.169, step = 4801 (0.936 sec)
INFO:tensorflow:global_step/sec: 87.4236
INFO:tensorflow:loss = 10333.318, step = 4901 (1.147 sec)
INFO:tensorflow:global_step/sec: 127.855
INFO:tensorflow:loss = 15173.955, step = 5001 (0.782 sec)
INFO:tensorflow:global_step/sec: 112.028
INFO:tensorflow:loss = 12940.822, step = 5101 (0.890 sec)
INFO:tensorflow:global_step/sec: 100.607
INFO:tensorflow:loss = 10854.685, step = 5201 (0.997 sec)
INFO:tensorflow:global_step/sec: 121.355
INFO:tensorflow:loss = 14676.209, step = 5301 (0.822 sec)
INFO:tensorflow:global_step/sec: 95.9606
INFO:tensorflow:loss = 13156.73, step = 5401 (1.043 sec)
INFO:tensorflow:global_step/sec: 118.042
INFO:tensorflow:loss = 17359.934, step = 5501 (0.847 sec)
INFO:tensorflow:global_step/sec: 109.939
INFO:tensorflow:loss = 8592.682, step = 5601 (0.910 sec)
INFO:tensorflow:global_step/sec: 104.666
INFO:tensorflow:loss = 11916.723, step = 5701 (0.956 sec)
INFO:tensorflow:global_step/sec: 110.305
INFO:tensorflow:loss = 17452.98, step = 5801 (0.906 sec)
INFO:tensorflow:global_step/sec: 91.3437
INFO:tensorflow:loss = 20825.383, step = 5901 (1.095 sec)
INFO:tensorflow:global_step/sec: 121.12
INFO:tensorflow:loss = 4669.6084, step = 6001 (0.825 sec)
INFO:tensorflow:Saving checkpoints for 6071 into taxi_trained/model.ckpt.
INFO:tensorflow:Loss for final step: 4455.771.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2019-07-01T20:22:40Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from taxi_trained/model.ckpt-6071
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2019-07-01-20:22:41
INFO:tensorflow:Saving dict for global step 6071: average_loss = 139.01192, global_step = 6071, label/mean = 11.666427, loss = 16532.488, prediction/mean = 6.1680436
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6071: taxi_trained/model.ckpt-6071
RMSE on dataset = 11.790331840515137

We are not beating our benchmark with either model ... what's up? Well, we may be using TensorFlow for Machine Learning, but we are not yet using it well. That's what the rest of this course is about!

But, for the record, let's say we had to choose between the two models. We'd choose the one with the lower validation error. Finally, we'd measure the RMSE on the test data with this chosen model.

Benchmark dataset

Let's do this on the benchmark dataset.

In [11]:
from google.cloud import bigquery
import numpy as np
import pandas as pd

def create_query(phase, EVERY_N):
  """
  phase: 1 = train 2 = valid
  """
  base_query = """
SELECT
  (tolls_amount + fare_amount) AS fare_amount,
  EXTRACT(DAYOFWEEK FROM pickup_datetime) * 1.0 AS dayofweek,
  EXTRACT(HOUR FROM pickup_datetime) * 1.0 AS hourofday,
  pickup_longitude AS pickuplon,
  pickup_latitude AS pickuplat,
  dropoff_longitude AS dropofflon,
  dropoff_latitude AS dropofflat,
  passenger_count*1.0 AS passengers,
  CONCAT(CAST(pickup_datetime AS STRING), CAST(pickup_longitude AS STRING), CAST(pickup_latitude AS STRING), CAST(dropoff_latitude AS STRING), CAST(dropoff_longitude AS STRING)) AS key
FROM
  `nyc-tlc.yellow.trips`
WHERE
  trip_distance > 0
  AND fare_amount >= 2.5
  AND pickup_longitude > -78
  AND pickup_longitude < -70
  AND dropoff_longitude > -78
  AND dropoff_longitude < -70
  AND pickup_latitude > 37
  AND pickup_latitude < 45
  AND dropoff_latitude > 37
  AND dropoff_latitude < 45
  AND passenger_count > 0
  """

  if EVERY_N == None:
    if phase < 2:
      # Training
      query = "{0} AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 4)) < 2".format(base_query)
    else:
      # Validation
      query = "{0} AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 4)) = {1}".format(base_query, phase)
  else:
    query = "{0} AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), {1})) = {2}".format(base_query, EVERY_N, phase)
    
  return query

query = create_query(2, 100000)
df = bigquery.Client().query(query).to_dataframe()
In [12]:
print_rmse(model, df)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2019-07-01T20:24:30Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from taxi_trained/model.ckpt-6071
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2019-07-01-20:24:31
INFO:tensorflow:Saving dict for global step 6071: average_loss = 114.547554, global_step = 6071, label/mean = 11.232336, loss = 14554.199, prediction/mean = 6.168037
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6071: taxi_trained/model.ckpt-6071
RMSE on dataset = 10.702689170837402

RMSE on benchmark dataset is 9.41 (your results will vary because of random seeds).

This is not only way more than our original benchmark of 6.00, but it doesn't even beat our distance-based rule's RMSE of 8.02.

Fear not -- you have learned how to write a TensorFlow model, but not to do all the things that you will have to do to your ML model performant. We will do this in the next chapters. In this chapter though, we will get our TensorFlow model ready for these improvements.

In a software sense, the rest of the labs in this chapter will be about refactoring the code so that we can improve it.

Challenge Exercise

Create a neural network that is capable of finding the volume of a cylinder given the radius of its base (r) and its height (h). Assume that the radius and height of the cylinder are both in the range 0.5 to 2.0. Simulate the necessary training dataset.

Hint (highlight to see):

The input features will be r and h and the label will be $\pi r^2 h$ Create random values for r and h and compute V. Your dataset will consist of r, h and V. Then, use a DNN regressor. Make sure to generate enough data.

Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License