对creditcard.csv使用Python中的TensorFlow进行深度学习建模。
时间: 2023-05-31 15:06:15 浏览: 73
首先,我们需要导入必要的库和数据集:
```python
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
data = pd.read_csv('creditcard.csv')
```
然后,我们需要对数据进行预处理。我们将使用标准化来缩放特征数据:
```python
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data['Amount'] = scaler.fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time'], axis=1)
```
接下来,我们将数据集分成训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(data.drop(['Class'], axis=1),
data['Class'],
test_size=0.2,
random_state=42)
```
然后,我们将使用TensorFlow建立模型。我们将使用顺序模型,并添加几个密集层和Dropout层:
```python
model = tf.keras.Sequential([
tf.keras.layers.Dense(256, input_shape=(X_train.shape[1],), activation='relu'),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(1, activation='sigmoid')
])
```
接下来,我们需要编译模型,并指定损失函数、优化器和评估指标:
```python
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
```
现在,我们可以使用训练数据拟合模型:
```python
model.fit(X_train, y_train, epochs=20, batch_size=1024, validation_split=0.2)
```
最后,我们可以使用测试数据评估模型:
```python
model.evaluate(X_test, y_test)
```
完整的代码如下:
```python
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
data = pd.read_csv('creditcard.csv')
scaler = StandardScaler()
data['Amount'] = scaler.fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(data.drop(['Class'], axis=1),
data['Class'],
test_size=0.2,
random_state=42)
model = tf.keras.Sequential([
tf.keras.layers.Dense(256, input_shape=(X_train.shape[1],), activation='relu'),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=20, batch_size=1024, validation_split=0.2)
model.evaluate(X_test, y_test)
```