PythonKerasValueError：資料基數不明確

我正在嘗試制作一個可以預測信用卡交易是否欺詐的模型。我的資料集在 Kaggle上可用。一切正常，直到我適合我的模型，當我收到此錯誤時：

ValueError: Data cardinality is ambiguous:
  x sizes: 7433462
  y sizes: 284807
Make sure all arrays contain the same number of samples.

有人可以幫我弄清楚出了什么問題嗎？

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential 
from tensorflow.keras.layers import Activation, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler

data = pd.read_csv("creditcard.csv")
trainSamples = data['Class']
labels = ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',  'V12', 'V13', 'V14', 'V15', 'V16',  'V17', 'V18', 'V19', 'V20', 'V21',  'V22',  'V23',  'V24',  'V25',  'V26',  'V27',  'V28',  'Amount']
trainSamples = data[labels]

trainLabels = np.array(trainLabels)
trainSamples = np.array(trainSamples)

trainLabels = shuffle(trainLabels)
trainSamples = shuffle(trainSamples)

scaler = MinMaxScaler(feature_range = (0, 1))
scaledTrainSample = scaler.fit_transform(trainSamples.reshape(-1,1))

model = Sequential([
    Dense(units = 16, input_shape = (1, ), activation = 'relu'),
    Dense(units = 32, activation = 'relu'),    
    Dense(units = 2, activation = 'softmax')
])

model.compile(optimizer = Adam(learning_rate = 0.0001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
model.fit(x = scaledTrainSample, y = trainLabels, validation_split = 0.1, batch_size = 10, epochs = 300, verbose = 2)

uj5u.com熱心網友回復：

您的代碼的主要問題是模型的輸入形狀應該是 30 而不是 1，因為您有 30 個特征，而輸出形狀應該是 1 而不是 2，因為您只有一個二進制標簽（即只有兩個類，0 或 1 ）。還有一些其他錯誤在下面的代碼中得到了糾正。

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler
tf.random.set_seed(0)

# import the data
df = pd.read_csv('creditcard.csv')

# extract the features and target
X = df.drop(labels=['Class'], axis=1).values
y = df['Class'].values

# count the number of classes
print(np.unique(y))
# [0 1]

# shuffle the data
X, y = shuffle(X, y, random_state=42)

# scale the features
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)

# build the model
model = Sequential([
    Dense(units=16, activation='relu', input_shape=(X.shape[1], )),
    Dense(units=32, activation='relu'),
    Dense(units=1, activation='sigmoid')
])

# fit the model
model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x=X, y=y, validation_split=0.1, batch_size=256, epochs=3)
# Epoch 1/3
# 1002/1002 [==============================] - 1s 761us/step - loss: 0.1787 - accuracy: 0.9983 - val_loss: 0.0193 - val_accuracy: 0.9981
# Epoch 2/3
# 1002/1002 [==============================] - 1s 684us/step - loss: 0.0136 - accuracy: 0.9983 - val_loss: 0.0130 - val_accuracy: 0.9981
# Epoch 3/3
# 1002/1002 [==============================] - 1s 680us/step - loss: 0.0119 - accuracy: 0.9983 - val_loss: 0.0127 - val_accuracy: 0.9981

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