Datasets:
HenryAI
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KerasCodeExamples.txt

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for _, group_data in data.groupby(\"Cover_Type\"):
random_selection = np.random.rand(len(group_data.index)) <= 0.85
train_splits.append(group_data[random_selection])
test_splits.append(group_data[~random_selection])
train_data = pd.concat(train_splits).sample(frac=1).reset_index(drop=True)
test_data = pd.concat(test_splits).sample(frac=1).reset_index(drop=True)
print(f\"Train split size: {len(train_data.index)}\")
print(f\"Test split size: {len(test_data.index)}\")
Train split size: 493323
Test split size: 87689
Next, store the training and test data in separate CSV files.
train_data_file = \"train_data.csv\"
test_data_file = \"test_data.csv\"
train_data.to_csv(train_data_file, index=False)
test_data.to_csv(test_data_file, index=False)
Define dataset metadata
Here, we define the metadata of the dataset that will be useful for reading and parsing the data into input features, and encoding the input features with respect to their types.
TARGET_FEATURE_NAME = \"Cover_Type\"
TARGET_FEATURE_LABELS = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]
NUMERIC_FEATURE_NAMES = [
\"Aspect\",
\"Elevation\",
\"Hillshade_3pm\",
\"Hillshade_9am\",
\"Hillshade_Noon\",
\"Horizontal_Distance_To_Fire_Points\",
\"Horizontal_Distance_To_Hydrology\",
\"Horizontal_Distance_To_Roadways\",
\"Slope\",
\"Vertical_Distance_To_Hydrology\",
]
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
\"Soil_Type\": list(data[\"Soil_Type\"].unique()),
\"Wilderness_Area\": list(data[\"Wilderness_Area\"].unique()),
}
CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES
COLUMN_DEFAULTS = [
[0] if feature_name in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME] else [\"NA\"]
for feature_name in CSV_HEADER
]
NUM_CLASSES = len(TARGET_FEATURE_LABELS)
Experiment setup
Next, let's define an input function that reads and parses the file, then converts features and labels into atf.data.Dataset for training or evaluation.
def get_dataset_from_csv(csv_file_path, batch_size, shuffle=False):
dataset = tf.data.experimental.make_csv_dataset(
csv_file_path,
batch_size=batch_size,
column_names=CSV_HEADER,
column_defaults=COLUMN_DEFAULTS,
label_name=TARGET_FEATURE_NAME,
num_epochs=1,
header=True,
shuffle=shuffle,
)
return dataset.cache()
Here we configure the parameters and implement the procedure for running a training and evaluation experiment given a model.
learning_rate = 0.001
dropout_rate = 0.1
batch_size = 265
num_epochs = 50
hidden_units = [32, 32]
def run_experiment(model):
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)
test_dataset = get_dataset_from_csv(test_data_file, batch_size)
print(\"Start training the model...\")
history = model.fit(train_dataset, epochs=num_epochs)
print(\"Model training finished\")
_, accuracy = model.evaluate(test_dataset, verbose=0)
print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")