Моя текущая задача - выполнить двоичную классификацию с использованием как числовых, так и порядковых категориальных данных.
Для этой задачи я эффективно воспроизвел код из следующего руководства Tensorflow: https://www.tensorflow.org/tutorials/structured_data/feature_columns
В настоящее время я изо всех сил пытаюсь найти причину, по которой возникает следующая ошибка, и как ее исправить.
Ошибка:
ValueError: slice index 0 of dimension 0 out of bounds. for 'strided_slice' (op: 'StridedSlice') with input shapes: [0], [1], [1], [1] and with computed input tensors: input[1] = <0>, input[2] = <1>, input[3] = <1>.
Код:
from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('target').astype('float64')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
scaler = StandardScaler()
vals = ["Low", "Med", "High"]
# Categorical data preparation
# Creativity, Productivity, Optimism, Pessimism is ordinal categorical --> numerical
for c in features[2:]:
if c != "Creativity":
data[c] = pd.Categorical(data[c], categories = vals, ordered = True)
data[c] = data[c].cat.codes / 2
else:
data[c] = pd.Categorical(data[c], categories = ["No", "Yes"], ordered = False)
data[c] = data[c].cat.codes.astype('float64')
data.loc[:, ["Social", "Exercise"]] = scaler.fit_transform(X = data.loc[:, ["Social", "Exercise"]].values)
# Splitting Data
train, test = train_test_split(data, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
# Tried reducing batch size as per a solution to a similar StackOverflow query
batch_size = 2
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
feature_columns = []
# numeric cols
for header in features:
feature_columns.append(feature_column.numeric_column(header))
# This is to concatenate all of these features for each example into single vectors
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
# Model Architecture
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dropout(.1),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(train_ds,validation_data=val_ds, epochs=10)
Данные
Every feature: ['Creativity', 'Productivity', 'Optimism', 'Pessimism']
A batch of Creativity: tf.Tensor([0. 0.], shape=(2,), dtype=float64)
A batch of Productivity: tf.Tensor([0.5 0.5], shape=(2,), dtype=float64)
A batch of Optimism: tf.Tensor([-0.5 -0.5], shape=(2,), dtype=float64)
A batch of Pessimism: tf.Tensor([-0.5 -0.5], shape=(2,), dtype=float64)
A batch of targets: tf.Tensor([0. 0.], shape=(2,), dtype=float64)
Любая помощь в понимании того, где возникла эта ошибка и как ее решить, будет fantasti c!
** РЕДАКТИРОВАТЬ: ** После запуска в Google Colaboratory я получаю эту ошибку при запуске model.fit()
ValueError: Feature (key: Creativity) cannot have rank 0. Given: Tensor("sequential_2/Cast:0", shape=(), dtype=float32)