这是一个多类分类任务。目标属性是“Class”。你知道吗

我的代码如下-# Column names to be used for training and testing sets-

col_names = ['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'Class']

# Read in training and testing datasets-

training_data = pd.read_csv("shuttle_training.csv", delimiter = ' ', names = col_names)

testing_data = pd.read_csv("shuttle_test.csv", delimiter = ' ', names = col_names)

print("\nTraining data dimension = {0} and testing data dimension = {1}\n".format(training_data.shape, testing_data.shape))

# Training data dimension = (43500, 10) and testing data dimension = (14500, 10)

# Data Preprocessing-

# Check for missing value(s) in training data-

training_data.isnull().values.any()

# False

# Get target attribute class distribution-

training_data["Class"].value_counts()

'''

1 34108

4 6748

5 2458

3 132

2 37

7 11

6 6

Name: Class, dtype: int64

'''

# NOTE: Majority of instances belong to class 1

# Visualizing the distribution of each attribute in dataset using boxplots-

fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')

sns.boxplot(data = training_data)

plt.xticks(rotation = 20)

plt.show()

# # To divide the data into attributes and labels, execute the following code:

# 'X' contains attributes

X = training_data.drop('Class', axis = 1)

# Convert 'X' to float-

X = X.values.astype("float")

# 'y' contains labels

y = training_data['Class']

# Normalize features (X)-

rb_scaler = RobustScaler()

X_std = rb_scaler.fit_transform(X)

# Divide attributes & labels into training & testing sets-

X_train, X_test, y_train, y_test = train_test_split(X_std, y, test_size = 0.30, stratify = y)

print("\nDimensions of training and testing sets are:")

print("X_train = {0}, y_train = {1}, X_test = {2} and y_test = {3}\n\n".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))

# Dimensions of training and testing sets are:

# X_train = (30450, 9), y_train = (30450,), X_test = (13050, 9) and y_test = (13050,)

from keras.models import Sequential

from keras.layers import Dense

from sklearn.model_selection import StratifiedKFold

from sklearn.pipeline import Pipeline

from keras.wrappers.scikit_learn import KerasClassifier

from sklearn.model_selection import cross_val_score

# Create Neural Network model-

model = Sequential()

# Input layer-

model.add(Dense(9, input_dim = 9, kernel_initializer = 'normal', activation = 'relu'))

# Hidden layer(s)-

model.add(Dense(9, kernel_initializer = 'normal', activation='relu'))

# Output layer-

model.add(Dense(7, activation = 'softmax')) # 7 output neurons for 7 classes in target attribute

# Compile NN model-

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

'''

Layer (type) Output Shape Param #

=================================================================

dense_1 (Dense) (None, 9) 90

_________________________________________________________________

dense_2 (Dense) (None, 9) 90

_________________________________________________________________

dense_3 (Dense) (None, 7) 70

=================================================================

Total params: 250

Trainable params: 250

Non-trainable params: 0

_________________________________________________________________

''# Train model on training data-

history = model.fit(X_train, y_train, epochs = 200, batch_size = 50, validation_data = (X_test, y_test), verbose = 1, shuffle = False)

它给了我一个错误-ValueError: Error when checking target: expected dense_3 to have shape (7,) but got array with shape (1,)

好吧，根据‘Class’属性(这是我们的目标)，似乎总共有7个类(尽管存在严重的类不平衡)。为什么我会有这个错误？有什么线索吗？你知道吗

谢谢！你知道吗

错误跟踪-

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