Why the keras do not take my data from CSV - csv

Question

I am following the tutorial about import CVS data into tensorflow.
I followed every step.
How ever it does not works out.
It showed that one of my feature is not in the dictionary.
If so, how can i put it into dictionary
The link to my colab
The code is write below and the error information is written in the end
import functools
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
train_file_path = "/content/Productivity training.csv"
test_file_path = "/content/Productivity Testing.csv"
LABEL_COLUMN = 'Productivity'
def get_dataset(file_path):
dataset = tf.data.experimental.make_csv_dataset(
file_path,
batch_size=12,
label_name=LABEL_COLUMN,
na_value="?",
num_epochs=1,
ignore_errors=True)
return dataset
raw_train_data = get_dataset(train_file_path)
raw_test_data = get_dataset(test_file_path)
examples, labels = next(iter(raw_train_data)) # 第一个批次
print("EXAMPLES: \n", examples, "\n")
print("LABELS: \n", labels)
CATEGORIES = {
'over working hours': ['0', '2'],
'experience' : ['0.5', '0.75', '1'],
'absent' : ['0.5', '0.25', '0']
}
categorical_columns = []
for feature, vocab in CATEGORIES.items():
cat_col = tf.feature_column.categorical_column_with_vocabulary_list(
key=feature, vocabulary_list=vocab)
categorical_columns.append(tf.feature_column.indicator_column(cat_col))
def process_continuous_data(mean, data):
# 标准化数据
data = tf.cast(data, tf.float32) * 1/(2*mean)
return tf.reshape(data, [-1, 1])
MEANS = {
'Weekday' : 4,
'highest' : 9.2540,
'lowest' : 3.47,
'Weather' : 2.63,
'Wind speed': 2.31
}
numerical_columns = []
for feature in MEANS.keys():
num_col = tf.feature_column.numeric_column(feature, normalizer_fn=functools.partial(process_continuous_data, MEANS[feature]))
numerical_columns.append(num_col)
preprocessing_layer = tf.keras.layers.DenseFeatures(categorical_columns+numerical_columns)
model = tf.keras.Sequential([
preprocessing_layer,
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid'),
])
model.compile(
loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
train_data = raw_train_data
test_data = raw_test_data
model.fit(train_data, epochs=20)
Here is the error info:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:389 call
outputs = layer(inputs, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/feature_column/dense_features.py:169 call **
self._state_manager)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2592 get_dense_tensor
return transformation_cache.get(self, state_manager)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2355 get
transformed = column.transform_feature(self, state_manager)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2564 transform_feature
input_tensor = transformation_cache.get(self.key, state_manager)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:2339 get
raise ValueError('Feature {} is not in features dictionary.'.format(key))
ValueError: Feature Weather is not in features dictionary.