def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x,train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Provide the training input dataset.
train_input_fn = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
# Provide the validation input dataset.
test_input_fn = automobile_data.make_dataset(args.batch_size, test_x, test_y)
# Use the same categorical columns as in `linear_regression_categorical`
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make_column = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, categorical columns must be converted from
# sparse to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style_column),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make_column, dimension=3),
]
# Build a DNNRegressor, with 2x20-unit hidden layers, with the feature columns
# defined above as input.
model = tf.estimator.DNNRegressor(
hidden_units=[20, 20], feature_columns=feature_columns)
# Train the model.
# By default, the Estimators log output every 100 steps.
model.train(input_fn=train_input_fn, steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=test_input_fn)
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(args.price_norm_factor * average_loss**0.5))
print()
def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x, train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Provide the training input dataset.
train_input_fn = automobile_data.make_dataset(args.batch_size, train_x,
train_y, True, 1000)
# Provide the validation input dataset.
test_input_fn = automobile_data.make_dataset(args.batch_size, test_x,
test_y)
# Use the same categorical columns as in `linear_regression_categorical`
body_style_vocab = [
"hardtop", "wagon", "sedan", "hatchback", "convertible"
]
body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make_column = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, categorical columns must be converted from sparse to dense.
# Wrap them in an `indicator_column` to create a one-hot vector from the input.
tf.feature_column.indicator_column(body_style_column),
# Or use an `embedding_column` to create a trainable vector for each index.
tf.feature_column.embedding_column(make_column, dimension=3),
]
# Build a DNNRegressor, with 2x20-unit hidden layers, with the feature columns defined above as input.
model = tf.estimator.DNNRegressor(hidden_units=[20, 20],
feature_columns=feature_columns)
# Train the model.
# By default, the Estimators log output every 100 steps.
model.train(input_fn=train_input_fn, steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=test_input_fn)
# The evaluation returns a Python dictionary. The "average_loss" key holds the Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}".format(
args.price_norm_factor * average_loss**0.5))
print()
def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x,train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Build the training dataset.
train = (
automobile_data.make_dataset(train_x, train_y)
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
.shuffle(1000).batch(args.batch_size)
# Repeat forever
.repeat())
# Build the validation dataset.
test = automobile_data.make_dataset(test_x, test_y).batch(args.batch_size)
# The following code demonstrates two of the ways that `feature_columns` can
# be used to build a model with categorical inputs.
# The first way assigns a unique weight to each category. To do this, you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero).
# Alternatively, you can define the vocabulary in a file (by calling
# `categorical_column_with_vocabulary_file`) or as a range of positive
# integers (by calling `categorical_column_with_identity`)
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
# The second way, appropriate for an unspecified vocabulary, is to create a
# hashed column. It will create a fixed length list of weights, and
# automatically assign each input category to a weight. Due to the
# pseudo-randomness of the process, some weights may be shared between
# categories, while others will remain unused.
make_column = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
# This model uses the same two numeric features as `linear_regressor.py`
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# This model adds two categorical colums that will adjust the price based
# on "make" and "body-style".
body_style_column,
make_column,
]
# Build the Estimator.
model = tf.estimator.LinearRegressor(feature_columns=feature_columns)
# Train the model.
# By default, the Estimators log output every 100 steps.
model.train(input_fn=from_dataset(train), steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=from_dataset(test))
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(args.price_norm_factor * average_loss**0.5))
print()
def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x, train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Provide the training input dataset.
train_input_fn = automobile_data.make_dataset(args.batch_size, train_x,
train_y, True, 1000)
# Provide the validation input dataset.
test_input_fn = automobile_data.make_dataset(args.batch_size, test_x,
test_y)
feature_columns = [
# "curb-weight" and "highway-mpg" are numeric columns.
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
]
# Build the Estimator.
model = tf.estimator.LinearRegressor(feature_columns=feature_columns)
# Train the model.
# By default, the Estimators log output every 100 steps.
model.train(input_fn=train_input_fn, steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=test_input_fn)
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}".format(
args.price_norm_factor * average_loss**0.5))
# Run the model in prediction mode.
input_dict = {
"curb-weight": np.array([2000, 3000]),
"highway-mpg": np.array([30, 40])
}
# Provide the predict input dataset.
predict_input_fn = automobile_data.make_dataset(1, input_dict)
predict_results = model.predict(input_fn=predict_input_fn)
# Print the prediction results.
print("\nPrediction results:")
for i, prediction in enumerate(predict_results):
msg = ("Curb weight: {: 4d}lbs, "
"Highway: {: 0d}mpg, "
"Prediction: ${: 9.2f}")
msg = msg.format(input_dict["curb-weight"][i],
input_dict["highway-mpg"][i],
args.price_norm_factor * prediction["predictions"][0])
print(" " + msg)
print()
def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x,train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Provide the training input dataset.
train_input_fn = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)
# Provide the validation input dataset.
test_input_fn = automobile_data.make_dataset(args.batch_size, test_x, test_y)
feature_columns = [
# "curb-weight" and "highway-mpg" are numeric columns.
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
]
# Build the Estimator.
model = tf.estimator.LinearRegressor(feature_columns=feature_columns)
# Train the model.
# By default, the Estimators log output every 100 steps.
model.train(input_fn=train_input_fn, steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=test_input_fn)
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(args.price_norm_factor * average_loss**0.5))
# Run the model in prediction mode.
input_dict = {
"curb-weight": np.array([2000, 3000]),
"highway-mpg": np.array([30, 40])
}
# Provide the predict input dataset.
predict_input_fn = automobile_data.make_dataset(1, input_dict)
predict_results = model.predict(input_fn=predict_input_fn)
# Print the prediction results.
print("\nPrediction results:")
for i, prediction in enumerate(predict_results):
msg = ("Curb weight: {: 4d}lbs, "
"Highway: {: 0d}mpg, "
"Prediction: ${: 9.2f}")
msg = msg.format(input_dict["curb-weight"][i], input_dict["highway-mpg"][i],
args.price_norm_factor * prediction["predictions"][0])
print(" " + msg)
print()
def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x, train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Provide the training input dataset.
train_input_fn = automobile_data.make_dataset(args.batch_size, train_x,
train_y, True, 1000)
# Build the validation dataset.
test_input_fn = automobile_data.make_dataset(args.batch_size, test_x,
test_y)
# The first way assigns a unique weight to each category. To do this you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero). Here we specify the vocabulary using a list of
# options. The vocabulary can also be specified with a vocabulary file (using
# `categorical_column_with_vocabulary_file`). For features covering a
# range of positive integers use `categorical_column_with_identity`.
body_style_vocab = [
"hardtop", "wagon", "sedan", "hatchback", "convertible"
]
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, convert categorical columns from sparse
# to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make, dimension=3),
]
# Build a custom Estimator, using the model_fn.
# `params` is passed through to the `model_fn`.
model = tf.estimator.Estimator(model_fn=my_dnn_regression_fn,
params={
"feature_columns": feature_columns,
"learning_rate": 0.001,
"optimizer": tf.train.AdamOptimizer,
"hidden_units": [20, 20]
})
# Train the model.
model.train(input_fn=train_input_fn, steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=test_input_fn)
# Print the Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}".format(
args.price_norm_factor * eval_result["rmse"]))
print()
def main(argv):
"""Builds, trains, and evaluates the model."""
args = parser.parse_args(argv[1:])
(train_x,train_y), (test_x, test_y) = automobile_data.load_data()
train_y /= args.price_norm_factor
test_y /= args.price_norm_factor
# Build the training dataset.
train = (
automobile_data.make_dataset(train_x, train_y)
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
.shuffle(1000).batch(args.batch_size)
# Repeat forever
.repeat())
# Build the validation dataset.
test = automobile_data.make_dataset(test_x, test_y).batch(args.batch_size)
# The first way assigns a unique weight to each category. To do this you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero). Here we specify the vocabulary using a list of
# options. The vocabulary can also be specified with a vocabulary file (using
# `categorical_column_with_vocabulary_file`). For features covering a
# range of positive integers use `categorical_column_with_identity`.
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, convert categorical columns from sparse
# to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make, dimension=3),
]
# Build a custom Estimator, using the model_fn.
# `params` is passed through to the `model_fn`.
model = tf.estimator.Estimator(
model_fn=my_dnn_regression_fn,
params={
"feature_columns": feature_columns,
"learning_rate": 0.001,
"optimizer": tf.train.AdamOptimizer,
"hidden_units": [20, 20]
})
# Train the model.
model.train(input_fn=from_dataset(train), steps=args.train_steps)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=from_dataset(test))
# Print the Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(args.price_norm_factor * eval_result["rmse"]))
print()