原文：https://tensorflow.google.cn/tutorials/structured_data/imbalanced_data
This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the Credit Card Fraud Detection dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use Keras to define the model and class weights to help the model learn from the imbalanced data. .
This tutorial contains complete code to:
- Load a CSV file using Pandas.
- Create train, validation, and test sets.
- Define and train a model using Keras (including setting class weights).
- Evaluate the model using various metrics (including precision and recall).
- Try common techniques for dealing with imbalanced data like:
- Class weighting
- Oversampling
import tensorflow as tf
from tensorflow import keras
import os
import tempfile
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler mpl.rcParams['figure.figsize'] = (12, 10)
colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] Pandas is a Python library with many helpful utilities for loading and working with structured data and can be used to download CSVs into a dataframe.
Note: This dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available here and the page of the DefeatFraud project
file = tf.keras.utils
raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')
raw_df.head() <iframe src="/tutorials/structured_data/imbalanced_data_0c4f86214be77177376f118e8968bdb18f3142f4c62b4f0137535f9174ed1f27.frame" class="framebox inherit-locale " allowfullscreen="" is-upgraded=""></iframe>
raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe() <iframe src="/tutorials/structured_data/imbalanced_data_dedddcdfc9cacebea7201b6e966755a5c703c43f30512a418cd6a74384bbb502.frame" class="framebox inherit-locale " allowfullscreen="" is-upgraded=""></iframe>
Let's look at the dataset imbalance:
neg, pos = np.bincount(raw_df['Class'])
total = neg + pos
print('Examples:\n Total: {}\n Positive: {} ({:.2f}% of total)\n'.format(
total, pos, 100 * pos / total)) Examples:
Total: 284807
Positive: 492 (0.17% of total)This shows the small fraction of positive samples.
The raw data has a few issues. First the Time and Amount columns are too variable to use directly. Drop the Time column (since it's not clear what it means) and take the log of the Amount column to reduce its range.
cleaned_df = raw_df.copy()
# You don't want the `Time` column.
cleaned_df.pop('Time')
# The `Amount` column covers a huge range. Convert to log-space.
eps = 0.001 # 0 => 0.1¢
cleaned_df['Log Ammount'] = np.log(cleaned_df.pop('Amount')+eps) Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where overfitting is a significant concern from the lack of training data.
# Use a utility from sklearn to split and shuffle our dataset.
train_df, test_df = train_test_split(cleaned_df, test_size=0.2)
train_df, val_df = train_test_split(train_df, test_size=0.2)
# Form np arrays of labels and features.
train_labels = np.array(train_df.pop('Class'))
bool_train_labels = train_labels != 0
val_labels = np.array(val_df.pop('Class'))
test_labels = np.array(test_df.pop('Class'))
train_features = np.array(train_df)
val_features = np.array(val_df)
test_features = np.array(test_df) Normalize the input features using the sklearn StandardScaler. This will set the mean to 0 and standard deviation to 1.
Note: The StandardScaler is only fit using the train_features to be sure the model is not peeking at the validation or test sets.
scaler = StandardScaler()
train_features = scaler.fit_transform(train_features)
val_features = scaler.transform(val_features)
test_features = scaler.transform(test_features)
train_features = np.clip(train_features, -5, 5)
val_features = np.clip(val_features, -5, 5)
test_features = np.clip(test_features, -5, 5)
print('Training labels shape:', train_labels.shape)
print('Validation labels shape:', val_labels.shape)
print('Test labels shape:', test_labels.shape)
print('Training features shape:', train_features.shape)
print('Validation features shape:', val_features.shape)
print('Test features shape:', test_features.shape) Training labels shape: (182276,)
Validation labels shape: (45569,)
Test labels shape: (56962,)
Training features shape: (182276, 29)
Validation features shape: (45569, 29)
Test features shape: (56962, 29)Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.
Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:
- Do these distributions make sense?
- Yes. You've normalized the input and these are mostly concentrated in the
+/- 2range.
- Yes. You've normalized the input and these are mostly concentrated in the
- Can you see the difference between the distributions?
- Yes the positive examples contain a much higher rate of extreme values.
pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)
neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)
sns.jointplot(pos_df['V5'], pos_df['V6'],
kind='hex', xlim=(-5,5), ylim=(-5,5))
plt.suptitle("Positive distribution")
sns.jointplot(neg_df['V5'], neg_df['V6'],
kind='hex', xlim=(-5,5), ylim=(-5,5))
_ = plt.suptitle("Negative distribution") /home/kbuilder/.local/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
/home/kbuilder/.local/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
Define a function that creates a simple neural network with a densly connected hidden layer, a dropout layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent:
METRICS = [
keras.metrics.TruePositives(name='tp'),
keras.metrics.FalsePositives(name='fp'),
keras.metrics.TrueNegatives(name='tn'),
keras.metrics.FalseNegatives(name='fn'),
keras.metrics.BinaryAccuracy(name='accuracy'),
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.AUC(name='auc'),
]
def make_model(metrics=METRICS, output_bias=None):
if output_bias is not None:
output_bias = tf.keras.initializers.Constant(output_bias)
model = keras.Sequential([
keras.layers.Dense(
16, activation='relu',
input_shape=(train_features.shape[-1],)),
keras.layers.Dropout(0.5),
keras.layers.Dense(1, activation='sigmoid',
bias_initializer=output_bias),
])
model.compile(
optimizer=keras.optimizers.Adam(lr=1e-3),
loss=keras.losses.BinaryCrossentropy(),
metrics=metrics)
return model Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.
- False negatives and false positives are samples that were incorrectly classified
- True negatives and true positives are samples that were correctly classified
-
Accuracy is the percentage of examples correctly classified >
$\frac{\text{true samples} }{\text{total samples} }$ -
Precision is the percentage of predicted positives that were correctly classified >
$\frac{\text{true positives} }{\text{true positives + false positives} }$ -
Recall is the percentage of actual positives that were correctly classified >
$\frac{\text{true positives} }{\text{true positives + false negatives} }$ - AUC refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than a random negative sample.
Note: Accuracy is not a helpful metric for this task. You can 99.8%+ accuracy on this task by predicting False all the time.
Read more:
Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.
Note: this model will not handle the class imbalance well. You will improve it later in this tutorial.
EPOCHS = 100
BATCH_SIZE = 2048
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor='val_auc',
verbose=1,
patience=10,
mode='max',
restore_best_weights=True) model = make_model()
model.summary() Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 16) 480
_________________________________________________________________
dropout (Dropout) (None, 16) 0
_________________________________________________________________
dense_1 (Dense) (None, 1) 17
=================================================================
Total params: 497
Trainable params: 497
Non-trainable params: 0
_________________________________________________________________Test run the model:
model.predict(train_features[:10]) array([[0.14155667],
[0.08245954],
[0.14218365],
[0.09784216],
[0.15547438],
[0.04696527],
[0.20200476],
[0.16137381],
[0.08730176],
[0.04467227]], dtype=float32)These initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: A Recipe for Training Neural Networks: "init well"). This can help with initial convergence.
With the default bias initialization the loss should be about math.log(2) = 0.69314
results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)
print("Loss: {:0.4f}".format(results[0])) Loss: 0.1159The correct bias to set can be derived from:
initial_bias = np.log([pos/neg])
initial_bias array([-6.35935934])Set that as the initial bias, and the model will give much more reasonable initial guesses.
It should be near: pos/total = 0.0018
model = make_model(output_bias=initial_bias)
model.predict(train_features[:10]) array([[0.00172629],
[0.00338373],
[0.00397264],
[0.00406079],
[0.00449285],
[0.00492807],
[0.00254243],
[0.00370109],
[0.01771316],
[0.0022068 ]], dtype=float32)With this initialization the initial loss should be approximately:
results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)
print("Loss: {:0.4f}".format(results[0])) Loss: 0.0141This initial loss is about 50 times less than if would have been with naive initialization.
This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. This also makes it easier to read plots of the loss during training.
To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training.
initial_weights = os.path.join(tempfile.mkdtemp(), 'initial_weights')
model.save_weights(initial_weights) Before moving on, confirm quick that the careful bias initialization actually helped.
Train the model for 20 epochs, with and without this careful initialization, and compare the losses:
model = make_model()
model.load_weights(initial_weights)
model.layers[-1].bias.assign([0.0])
zero_bias_history = model.fit(
train_features,
train_labels,
batch_size=BATCH_SIZE,
epochs=20,
validation_data=(val_features, val_labels),
verbose=0) model = make_model()
model.load_weights(initial_weights)
careful_bias_history = model.fit(
train_features,
train_labels,
batch_size=BATCH_SIZE,
epochs=20,
validation_data=(val_features, val_labels),
verbose=0) def plot_loss(history, label, n):
# Use a log scale to show the wide range of values.
plt.semilogy(history.epoch, history.history['loss'],
color=colors[n], label='Train '+label)
plt.semilogy(history.epoch, history.history['val_loss'],
color=colors[n], label='Val '+label,
linestyle="--")
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend() plot_loss(zero_bias_history, "Zero Bias", 0)
plot_loss(careful_bias_history, "Careful Bias", 1) 
The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage.
model = make_model()
model.load_weights(initial_weights)
baseline_history = model.fit(
train_features,
train_labels,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
callbacks=[early_stopping],
validation_data=(val_features, val_labels)) Epoch 1/100
90/90 [==============================] - 1s 14ms/step - loss: 0.0127 - tp: 93.0000 - fp: 90.0000 - tn: 227362.0000 - fn: 300.0000 - accuracy: 0.9983 - precision: 0.5082 - recall: 0.2366 - auc: 0.8002 - val_loss: 0.0066 - val_tp: 8.0000 - val_fp: 4.0000 - val_tn: 45489.0000 - val_fn: 68.0000 - val_accuracy: 0.9984 - val_precision: 0.6667 - val_recall: 0.1053 - val_auc: 0.9394
Epoch 2/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0081 - tp: 99.0000 - fp: 25.0000 - tn: 181934.0000 - fn: 218.0000 - accuracy: 0.9987 - precision: 0.7984 - recall: 0.3123 - auc: 0.8446 - val_loss: 0.0044 - val_tp: 40.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 36.0000 - val_accuracy: 0.9991 - val_precision: 0.8696 - val_recall: 0.5263 - val_auc: 0.9471
Epoch 3/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0067 - tp: 147.0000 - fp: 29.0000 - tn: 181930.0000 - fn: 170.0000 - accuracy: 0.9989 - precision: 0.8352 - recall: 0.4637 - auc: 0.8739 - val_loss: 0.0036 - val_tp: 45.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 31.0000 - val_accuracy: 0.9992 - val_precision: 0.8824 - val_recall: 0.5921 - val_auc: 0.9472
Epoch 4/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0061 - tp: 151.0000 - fp: 32.0000 - tn: 181927.0000 - fn: 166.0000 - accuracy: 0.9989 - precision: 0.8251 - recall: 0.4763 - auc: 0.8867 - val_loss: 0.0031 - val_tp: 50.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 26.0000 - val_accuracy: 0.9993 - val_precision: 0.8929 - val_recall: 0.6579 - val_auc: 0.9472
Epoch 5/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0059 - tp: 162.0000 - fp: 35.0000 - tn: 181924.0000 - fn: 155.0000 - accuracy: 0.9990 - precision: 0.8223 - recall: 0.5110 - auc: 0.8970 - val_loss: 0.0028 - val_tp: 53.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.8983 - val_recall: 0.6974 - val_auc: 0.9538
Epoch 6/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0056 - tp: 167.0000 - fp: 34.0000 - tn: 181925.0000 - fn: 150.0000 - accuracy: 0.9990 - precision: 0.8308 - recall: 0.5268 - auc: 0.8942 - val_loss: 0.0026 - val_tp: 53.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.8983 - val_recall: 0.6974 - val_auc: 0.9670
Epoch 7/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0051 - tp: 179.0000 - fp: 32.0000 - tn: 181927.0000 - fn: 138.0000 - accuracy: 0.9991 - precision: 0.8483 - recall: 0.5647 - auc: 0.9023 - val_loss: 0.0024 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 18.0000 - val_accuracy: 0.9995 - val_precision: 0.9062 - val_recall: 0.7632 - val_auc: 0.9669
Epoch 8/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0049 - tp: 181.0000 - fp: 29.0000 - tn: 181930.0000 - fn: 136.0000 - accuracy: 0.9991 - precision: 0.8619 - recall: 0.5710 - auc: 0.9040 - val_loss: 0.0023 - val_tp: 59.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9077 - val_recall: 0.7763 - val_auc: 0.9735
Epoch 9/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0050 - tp: 181.0000 - fp: 36.0000 - tn: 181923.0000 - fn: 136.0000 - accuracy: 0.9991 - precision: 0.8341 - recall: 0.5710 - auc: 0.9025 - val_loss: 0.0022 - val_tp: 56.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9032 - val_recall: 0.7368 - val_auc: 0.9735
Epoch 10/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0048 - tp: 179.0000 - fp: 32.0000 - tn: 181927.0000 - fn: 138.0000 - accuracy: 0.9991 - precision: 0.8483 - recall: 0.5647 - auc: 0.9041 - val_loss: 0.0021 - val_tp: 57.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 19.0000 - val_accuracy: 0.9995 - val_precision: 0.9048 - val_recall: 0.7500 - val_auc: 0.9735
Epoch 11/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0044 - tp: 188.0000 - fp: 29.0000 - tn: 181930.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8664 - recall: 0.5931 - auc: 0.9246 - val_loss: 0.0020 - val_tp: 57.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 19.0000 - val_accuracy: 0.9995 - val_precision: 0.9048 - val_recall: 0.7500 - val_auc: 0.9735
Epoch 12/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0047 - tp: 168.0000 - fp: 34.0000 - tn: 181925.0000 - fn: 149.0000 - accuracy: 0.9990 - precision: 0.8317 - recall: 0.5300 - auc: 0.9184 - val_loss: 0.0019 - val_tp: 63.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9130 - val_recall: 0.8289 - val_auc: 0.9735
Epoch 13/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0045 - tp: 186.0000 - fp: 32.0000 - tn: 181927.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8532 - recall: 0.5868 - auc: 0.9105 - val_loss: 0.0019 - val_tp: 63.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9130 - val_recall: 0.8289 - val_auc: 0.9735
Epoch 14/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0043 - tp: 199.0000 - fp: 37.0000 - tn: 181922.0000 - fn: 118.0000 - accuracy: 0.9991 - precision: 0.8432 - recall: 0.6278 - auc: 0.9217 - val_loss: 0.0019 - val_tp: 59.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9077 - val_recall: 0.7763 - val_auc: 0.9735
Epoch 15/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0045 - tp: 180.0000 - fp: 28.0000 - tn: 181931.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8654 - recall: 0.5678 - auc: 0.9216 - val_loss: 0.0019 - val_tp: 63.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9130 - val_recall: 0.8289 - val_auc: 0.9801
Epoch 16/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0044 - tp: 188.0000 - fp: 41.0000 - tn: 181918.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8210 - recall: 0.5931 - auc: 0.9200 - val_loss: 0.0019 - val_tp: 52.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9455 - val_recall: 0.6842 - val_auc: 0.9735
Epoch 17/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0045 - tp: 178.0000 - fp: 29.0000 - tn: 181930.0000 - fn: 139.0000 - accuracy: 0.9991 - precision: 0.8599 - recall: 0.5615 - auc: 0.9153 - val_loss: 0.0018 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 10.0000 - val_accuracy: 0.9996 - val_precision: 0.9167 - val_recall: 0.8684 - val_auc: 0.9801
Epoch 18/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0043 - tp: 186.0000 - fp: 33.0000 - tn: 181926.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8493 - recall: 0.5868 - auc: 0.9248 - val_loss: 0.0018 - val_tp: 65.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 11.0000 - val_accuracy: 0.9996 - val_precision: 0.9155 - val_recall: 0.8553 - val_auc: 0.9735
Epoch 19/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0044 - tp: 188.0000 - fp: 28.0000 - tn: 181931.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8704 - recall: 0.5931 - auc: 0.9091 - val_loss: 0.0018 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 10.0000 - val_accuracy: 0.9996 - val_precision: 0.9167 - val_recall: 0.8684 - val_auc: 0.9801
Epoch 20/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0042 - tp: 189.0000 - fp: 30.0000 - tn: 181929.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8630 - recall: 0.5962 - auc: 0.9249 - val_loss: 0.0018 - val_tp: 63.0000 - val_fp: 5.0000 - val_tn: 45488.0000 - val_fn: 13.0000 - val_accuracy: 0.9996 - val_precision: 0.9265 - val_recall: 0.8289 - val_auc: 0.9735
Epoch 21/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0042 - tp: 185.0000 - fp: 35.0000 - tn: 181924.0000 - fn: 132.0000 - accuracy: 0.9991 - precision: 0.8409 - recall: 0.5836 - auc: 0.9248 - val_loss: 0.0017 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 10.0000 - val_accuracy: 0.9996 - val_precision: 0.9167 - val_recall: 0.8684 - val_auc: 0.9801
Epoch 22/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0042 - tp: 188.0000 - fp: 31.0000 - tn: 181928.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8584 - recall: 0.5931 - auc: 0.9249 - val_loss: 0.0017 - val_tp: 64.0000 - val_fp: 5.0000 - val_tn: 45488.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9275 - val_recall: 0.8421 - val_auc: 0.9801
Epoch 23/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0045 - tp: 175.0000 - fp: 39.0000 - tn: 181920.0000 - fn: 142.0000 - accuracy: 0.9990 - precision: 0.8178 - recall: 0.5521 - auc: 0.9169 - val_loss: 0.0017 - val_tp: 65.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 11.0000 - val_accuracy: 0.9996 - val_precision: 0.9155 - val_recall: 0.8553 - val_auc: 0.9801
Epoch 24/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0043 - tp: 188.0000 - fp: 31.0000 - tn: 181928.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8584 - recall: 0.5931 - auc: 0.9122 - val_loss: 0.0017 - val_tp: 64.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9143 - val_recall: 0.8421 - val_auc: 0.9801
Epoch 25/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0043 - tp: 179.0000 - fp: 29.0000 - tn: 181930.0000 - fn: 138.0000 - accuracy: 0.9991 - precision: 0.8606 - recall: 0.5647 - auc: 0.9311 - val_loss: 0.0017 - val_tp: 64.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9143 - val_recall: 0.8421 - val_auc: 0.9801
Epoch 26/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0041 - tp: 189.0000 - fp: 32.0000 - tn: 181927.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8552 - recall: 0.5962 - auc: 0.9218 - val_loss: 0.0017 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 10.0000 - val_accuracy: 0.9996 - val_precision: 0.9167 - val_recall: 0.8684 - val_auc: 0.9801
Epoch 27/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0040 - tp: 193.0000 - fp: 27.0000 - tn: 181932.0000 - fn: 124.0000 - accuracy: 0.9992 - precision: 0.8773 - recall: 0.6088 - auc: 0.9202 - val_loss: 0.0017 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45487.0000 - val_fn: 10.0000 - val_accuracy: 0.9996 - val_precision: 0.9167 - val_recall: 0.8684 - val_auc: 0.9801
Epoch 28/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0041 - tp: 189.0000 - fp: 31.0000 - tn: 181928.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8591 - recall: 0.5962 - auc: 0.9187 - val_loss: 0.0017 - val_tp: 62.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9538 - val_recall: 0.8158 - val_auc: 0.9801
Epoch 29/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0041 - tp: 173.0000 - fp: 35.0000 - tn: 181924.0000 - fn: 144.0000 - accuracy: 0.9990 - precision: 0.8317 - recall: 0.5457 - auc: 0.9233 - val_loss: 0.0017 - val_tp: 64.0000 - val_fp: 4.0000 - val_tn: 45489.0000 - val_fn: 12.0000 - val_accuracy: 0.9996 - val_precision: 0.9412 - val_recall: 0.8421 - val_auc: 0.9801
Epoch 30/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0041 - tp: 193.0000 - fp: 36.0000 - tn: 181923.0000 - fn: 124.0000 - accuracy: 0.9991 - precision: 0.8428 - recall: 0.6088 - auc: 0.9218 - val_loss: 0.0017 - val_tp: 62.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9538 - val_recall: 0.8158 - val_auc: 0.9801
Epoch 31/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0044 - tp: 190.0000 - fp: 35.0000 - tn: 181924.0000 - fn: 127.0000 - accuracy: 0.9991 - precision: 0.8444 - recall: 0.5994 - auc: 0.9122 - val_loss: 0.0017 - val_tp: 60.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 16.0000 - val_accuracy: 0.9996 - val_precision: 0.9524 - val_recall: 0.7895 - val_auc: 0.9801
Epoch 32/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0043 - tp: 184.0000 - fp: 33.0000 - tn: 181926.0000 - fn: 133.0000 - accuracy: 0.9991 - precision: 0.8479 - recall: 0.5804 - auc: 0.9186 - val_loss: 0.0017 - val_tp: 61.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 15.0000 - val_accuracy: 0.9996 - val_precision: 0.9531 - val_recall: 0.8026 - val_auc: 0.9801
Epoch 33/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0041 - tp: 188.0000 - fp: 36.0000 - tn: 181923.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8393 - recall: 0.5931 - auc: 0.9218 - val_loss: 0.0016 - val_tp: 62.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9538 - val_recall: 0.8158 - val_auc: 0.9801
Epoch 34/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0039 - tp: 196.0000 - fp: 37.0000 - tn: 181922.0000 - fn: 121.0000 - accuracy: 0.9991 - precision: 0.8412 - recall: 0.6183 - auc: 0.9297 - val_loss: 0.0017 - val_tp: 62.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9538 - val_recall: 0.8158 - val_auc: 0.9801
Epoch 35/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0040 - tp: 189.0000 - fp: 39.0000 - tn: 181920.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8289 - recall: 0.5962 - auc: 0.9281 - val_loss: 0.0017 - val_tp: 60.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 16.0000 - val_accuracy: 0.9996 - val_precision: 0.9524 - val_recall: 0.7895 - val_auc: 0.9801
Epoch 36/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0039 - tp: 197.0000 - fp: 29.0000 - tn: 181930.0000 - fn: 120.0000 - accuracy: 0.9992 - precision: 0.8717 - recall: 0.6215 - auc: 0.9203 - val_loss: 0.0016 - val_tp: 65.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 11.0000 - val_accuracy: 0.9997 - val_precision: 0.9559 - val_recall: 0.8553 - val_auc: 0.9801
Epoch 37/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0039 - tp: 195.0000 - fp: 26.0000 - tn: 181933.0000 - fn: 122.0000 - accuracy: 0.9992 - precision: 0.8824 - recall: 0.6151 - auc: 0.9234 - val_loss: 0.0016 - val_tp: 62.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9538 - val_recall: 0.8158 - val_auc: 0.9801
Epoch 38/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0038 - tp: 197.0000 - fp: 40.0000 - tn: 181919.0000 - fn: 120.0000 - accuracy: 0.9991 - precision: 0.8312 - recall: 0.6215 - auc: 0.9329 - val_loss: 0.0017 - val_tp: 59.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 17.0000 - val_accuracy: 0.9996 - val_precision: 0.9516 - val_recall: 0.7763 - val_auc: 0.9801
Epoch 39/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0040 - tp: 195.0000 - fp: 34.0000 - tn: 181925.0000 - fn: 122.0000 - accuracy: 0.9991 - precision: 0.8515 - recall: 0.6151 - auc: 0.9343 - val_loss: 0.0016 - val_tp: 62.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 14.0000 - val_accuracy: 0.9996 - val_precision: 0.9538 - val_recall: 0.8158 - val_auc: 0.9801
Epoch 40/100
90/90 [==============================] - 1s 6ms/step - loss: 0.0039 - tp: 199.0000 - fp: 35.0000 - tn: 181924.0000 - fn: 118.0000 - accuracy: 0.9992 - precision: 0.8504 - recall: 0.6278 - auc: 0.9250 - val_loss: 0.0016 - val_tp: 60.0000 - val_fp: 3.0000 - val_tn: 45490.0000 - val_fn: 16.0000 - val_accuracy: 0.9996 - val_precision: 0.9524 - val_recall: 0.7895 - val_auc: 0.9801
Epoch 41/100
88/90 [============================>.] - ETA: 0s - loss: 0.0041 - tp: 185.0000 - fp: 35.0000 - tn: 179875.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8409 - recall: 0.5892 - auc: 0.9305Restoring model weights from the end of the best epoch.
90/90 [==============================] - 1s 6ms/step - loss: 0.0041 - tp: 186.0000 - fp: 35.0000 - tn: 181924.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8416 - recall: 0.5868 - auc: 0.9295 - val_loss: 0.0016 - val_tp: 65.0000 - val_fp: 5.0000 - val_tn: 45488.0000 - val_fn: 11.0000 - val_accuracy: 0.9996 - val_precision: 0.9286 - val_recall: 0.8553 - val_auc: 0.9801
Epoch 00041: early stoppingIn this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in this tutorial.
Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example.
def plot_metrics(history):
metrics = ['loss', 'auc', 'precision', 'recall']
for n, metric in enumerate(metrics):
name = metric.replace("_"," ").capitalize()
plt.subplot(2,2,n+1)
plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')
plt.plot(history.epoch, history.history['val_'+metric],
color=colors[0], linestyle="--", label='Val')
plt.xlabel('Epoch')
plt.ylabel(name)
if metric == 'loss':
plt.ylim([0, plt.ylim()[1]])
elif metric == 'auc':
plt.ylim([0.8,1])
else:
plt.ylim([0,1])
plt.legend() plot_metrics(baseline_history) 
Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model.
You can use a confusion matrix to summarize the actual vs. predicted labels where the X axis is the predicted label and the Y axis is the actual label.
train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)
test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE) def plot_cm(labels, predictions, p=0.5):
cm = confusion_matrix(labels, predictions > p)
plt.figure(figsize=(5,5))
sns.heatmap(cm, annot=True, fmt="d")
plt.title('Confusion matrix @{:.2f}'.format(p))
plt.ylabel('Actual label')
plt.xlabel('Predicted label')
print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])
print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])
print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])
print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])
print('Total Fraudulent Transactions: ', np.sum(cm[1])) Evaluate your model on the test dataset and display the results for the metrics you created above.
baseline_results = model.evaluate(test_features, test_labels,
batch_size=BATCH_SIZE, verbose=0)
for name, value in zip(model.metrics_names, baseline_results):
print(name, ': ', value)
print()
plot_cm(test_labels, test_predictions_baseline) loss : 0.002797449706122279
tp : 68.0
fp : 3.0
tn : 56860.0
fn : 31.0
accuracy : 0.9994031190872192
precision : 0.9577465057373047
recall : 0.6868686676025391
auc : 0.949228823184967
Legitimate Transactions Detected (True Negatives): 56860
Legitimate Transactions Incorrectly Detected (False Positives): 3
Fraudulent Transactions Missed (False Negatives): 31
Fraudulent Transactions Detected (True Positives): 68
Total Fraudulent Transactions: 99
If the model had predicted everything perfectly, this would be a diagonal matrix where values off the main diagonal, indicating incorrect predictions, would be zero. In this case the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged. However, you would likely want to have even fewer false negatives despite the cost of increasing the number of false positives. This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity.
Now plot the ROC. This plot is useful because it shows, at a glance, the range of performance the model can reach just by tuning the output threshold.
def plot_roc(name, labels, predictions, **kwargs):
fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)
plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)
plt.xlabel('False positives [%]')
plt.ylabel('True positives [%]')
plt.xlim([-0.5,20])
plt.ylim([80,100.5])
plt.grid(True)
ax = plt.gca()
ax.set_aspect('equal') plot_roc("Train Baseline", train_labels, train_predictions_baseline, color=colors[0])
plot_roc("Test Baseline", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')
plt.legend(loc='lower right') <matplotlib.legend.Legend at 0x7fbcbc3cd0b8>
It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness.
The goal is to identify fraudulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to "pay more attention" to examples from an under-represented class.
# Scaling by total/2 helps keep the loss to a similar magnitude.
# The sum of the weights of all examples stays the same.
weight_for_0 = (1 / neg)*(total)/2.0
weight_for_1 = (1 / pos)*(total)/2.0
class_weight = {0: weight_for_0, 1: weight_for_1}
print('Weight for class 0: {:.2f}'.format(weight_for_0))
print('Weight for class 1: {:.2f}'.format(weight_for_1)) Weight for class 0: 0.50
Weight for class 1: 289.44Now try re-training and evaluating the model with class weights to see how that affects the predictions.
Note: Using class_weights changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like optimizers.SGD, may fail. The optimizer used here, optimizers.Adam, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models.
weighted_model = make_model()
weighted_model.load_weights(initial_weights)
weighted_history = weighted_model.fit(
train_features,
train_labels,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
callbacks=[early_stopping],
validation_data=(val_features, val_labels),
# The class weights go here
class_weight=class_weight) Epoch 1/100
90/90 [==============================] - 1s 14ms/step - loss: 2.0102 - tp: 135.0000 - fp: 420.0000 - tn: 238402.0000 - fn: 281.0000 - accuracy: 0.9971 - precision: 0.2432 - recall: 0.3245 - auc: 0.8079 - val_loss: 0.0111 - val_tp: 45.0000 - val_fp: 51.0000 - val_tn: 45442.0000 - val_fn: 31.0000 - val_accuracy: 0.9982 - val_precision: 0.4688 - val_recall: 0.5921 - val_auc: 0.9314
Epoch 2/100
90/90 [==============================] - 1s 6ms/step - loss: 0.9256 - tp: 178.0000 - fp: 793.0000 - tn: 181166.0000 - fn: 139.0000 - accuracy: 0.9949 - precision: 0.1833 - recall: 0.5615 - auc: 0.8662 - val_loss: 0.0164 - val_tp: 66.0000 - val_fp: 108.0000 - val_tn: 45385.0000 - val_fn: 10.0000 - val_accuracy: 0.9974 - val_precision: 0.3793 - val_recall: 0.8684 - val_auc: 0.9468
Epoch 3/100
90/90 [==============================] - 1s 6ms/step - loss: 0.7136 - tp: 215.0000 - fp: 1377.0000 - tn: 180582.0000 - fn: 102.0000 - accuracy: 0.9919 - precision: 0.1351 - recall: 0.6782 - auc: 0.8811 - val_loss: 0.0238 - val_tp: 68.0000 - val_fp: 174.0000 - val_tn: 45319.0000 - val_fn: 8.0000 - val_accuracy: 0.9960 - val_precision: 0.2810 - val_recall: 0.8947 - val_auc: 0.9866
Epoch 4/100
90/90 [==============================] - 1s 6ms/step - loss: 0.5158 - tp: 240.0000 - fp: 2141.0000 - tn: 179818.0000 - fn: 77.0000 - accuracy: 0.9878 - precision: 0.1008 - recall: 0.7571 - auc: 0.9124 - val_loss: 0.0334 - val_tp: 69.0000 - val_fp: 257.0000 - val_tn: 45236.0000 - val_fn: 7.0000 - val_accuracy: 0.9942 - val_precision: 0.2117 - val_recall: 0.9079 - val_auc: 0.9951
Epoch 5/100
90/90 [==============================] - 1s 6ms/step - loss: 0.4060 - tp: 256.0000 - fp: 3039.0000 - tn: 178920.0000 - fn: 61.0000 - accuracy: 0.9830 - precision: 0.0777 - recall: 0.8076 - auc: 0.9329 - val_loss: 0.0439 - val_tp: 72.0000 - val_fp: 364.0000 - val_tn: 45129.0000 - val_fn: 4.0000 - val_accuracy: 0.9919 - val_precision: 0.1651 - val_recall: 0.9474 - val_auc: 0.9965
Epoch 6/100
90/90 [==============================] - 1s 6ms/step - loss: 0.4045 - tp: 255.0000 - fp: 4034.0000 - tn: 177925.0000 - fn: 62.0000 - accuracy: 0.9775 - precision: 0.0595 - recall: 0.8044 - auc: 0.9289 - val_loss: 0.0557 - val_tp: 73.0000 - val_fp: 572.0000 - val_tn: 44921.0000 - val_fn: 3.0000 - val_accuracy: 0.9874 - val_precision: 0.1132 - val_recall: 0.9605 - val_auc: 0.9969
Epoch 7/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2990 - tp: 267.0000 - fp: 5024.0000 - tn: 176935.0000 - fn: 50.0000 - accuracy: 0.9722 - precision: 0.0505 - recall: 0.8423 - auc: 0.9544 - val_loss: 0.0723 - val_tp: 74.0000 - val_fp: 825.0000 - val_tn: 44668.0000 - val_fn: 2.0000 - val_accuracy: 0.9819 - val_precision: 0.0823 - val_recall: 0.9737 - val_auc: 0.9971
Epoch 8/100
90/90 [==============================] - 1s 6ms/step - loss: 0.3124 - tp: 267.0000 - fp: 6321.0000 - tn: 175638.0000 - fn: 50.0000 - accuracy: 0.9650 - precision: 0.0405 - recall: 0.8423 - auc: 0.9493 - val_loss: 0.0886 - val_tp: 74.0000 - val_fp: 1043.0000 - val_tn: 44450.0000 - val_fn: 2.0000 - val_accuracy: 0.9771 - val_precision: 0.0662 - val_recall: 0.9737 - val_auc: 0.9971
Epoch 9/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2856 - tp: 276.0000 - fp: 6879.0000 - tn: 175080.0000 - fn: 41.0000 - accuracy: 0.9620 - precision: 0.0386 - recall: 0.8707 - auc: 0.9536 - val_loss: 0.0963 - val_tp: 74.0000 - val_fp: 1120.0000 - val_tn: 44373.0000 - val_fn: 2.0000 - val_accuracy: 0.9754 - val_precision: 0.0620 - val_recall: 0.9737 - val_auc: 0.9972
Epoch 10/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2871 - tp: 272.0000 - fp: 7610.0000 - tn: 174349.0000 - fn: 45.0000 - accuracy: 0.9580 - precision: 0.0345 - recall: 0.8580 - auc: 0.9545 - val_loss: 0.1053 - val_tp: 74.0000 - val_fp: 1219.0000 - val_tn: 44274.0000 - val_fn: 2.0000 - val_accuracy: 0.9732 - val_precision: 0.0572 - val_recall: 0.9737 - val_auc: 0.9972
Epoch 11/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2646 - tp: 273.0000 - fp: 8061.0000 - tn: 173898.0000 - fn: 44.0000 - accuracy: 0.9555 - precision: 0.0328 - recall: 0.8612 - auc: 0.9602 - val_loss: 0.1079 - val_tp: 74.0000 - val_fp: 1242.0000 - val_tn: 44251.0000 - val_fn: 2.0000 - val_accuracy: 0.9727 - val_precision: 0.0562 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 12/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2574 - tp: 277.0000 - fp: 8239.0000 - tn: 173720.0000 - fn: 40.0000 - accuracy: 0.9546 - precision: 0.0325 - recall: 0.8738 - auc: 0.9621 - val_loss: 0.1086 - val_tp: 74.0000 - val_fp: 1223.0000 - val_tn: 44270.0000 - val_fn: 2.0000 - val_accuracy: 0.9731 - val_precision: 0.0571 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 13/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2525 - tp: 279.0000 - fp: 7925.0000 - tn: 174034.0000 - fn: 38.0000 - accuracy: 0.9563 - precision: 0.0340 - recall: 0.8801 - auc: 0.9604 - val_loss: 0.1032 - val_tp: 74.0000 - val_fp: 1153.0000 - val_tn: 44340.0000 - val_fn: 2.0000 - val_accuracy: 0.9747 - val_precision: 0.0603 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 14/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2712 - tp: 273.0000 - fp: 8290.0000 - tn: 173669.0000 - fn: 44.0000 - accuracy: 0.9543 - precision: 0.0319 - recall: 0.8612 - auc: 0.9572 - val_loss: 0.1083 - val_tp: 74.0000 - val_fp: 1193.0000 - val_tn: 44300.0000 - val_fn: 2.0000 - val_accuracy: 0.9738 - val_precision: 0.0584 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 15/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2324 - tp: 283.0000 - fp: 8182.0000 - tn: 173777.0000 - fn: 34.0000 - accuracy: 0.9549 - precision: 0.0334 - recall: 0.8927 - auc: 0.9668 - val_loss: 0.1017 - val_tp: 74.0000 - val_fp: 1131.0000 - val_tn: 44362.0000 - val_fn: 2.0000 - val_accuracy: 0.9751 - val_precision: 0.0614 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 16/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2605 - tp: 277.0000 - fp: 7798.0000 - tn: 174161.0000 - fn: 40.0000 - accuracy: 0.9570 - precision: 0.0343 - recall: 0.8738 - auc: 0.9585 - val_loss: 0.1030 - val_tp: 74.0000 - val_fp: 1134.0000 - val_tn: 44359.0000 - val_fn: 2.0000 - val_accuracy: 0.9751 - val_precision: 0.0613 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 17/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2235 - tp: 281.0000 - fp: 7994.0000 - tn: 173965.0000 - fn: 36.0000 - accuracy: 0.9559 - precision: 0.0340 - recall: 0.8864 - auc: 0.9696 - val_loss: 0.1029 - val_tp: 74.0000 - val_fp: 1127.0000 - val_tn: 44366.0000 - val_fn: 2.0000 - val_accuracy: 0.9752 - val_precision: 0.0616 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 18/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2455 - tp: 278.0000 - fp: 7706.0000 - tn: 174253.0000 - fn: 39.0000 - accuracy: 0.9575 - precision: 0.0348 - recall: 0.8770 - auc: 0.9633 - val_loss: 0.1010 - val_tp: 74.0000 - val_fp: 1099.0000 - val_tn: 44394.0000 - val_fn: 2.0000 - val_accuracy: 0.9758 - val_precision: 0.0631 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 19/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1759 - tp: 285.0000 - fp: 7766.0000 - tn: 174193.0000 - fn: 32.0000 - accuracy: 0.9572 - precision: 0.0354 - recall: 0.8991 - auc: 0.9813 - val_loss: 0.1001 - val_tp: 74.0000 - val_fp: 1079.0000 - val_tn: 44414.0000 - val_fn: 2.0000 - val_accuracy: 0.9763 - val_precision: 0.0642 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 20/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2220 - tp: 283.0000 - fp: 7554.0000 - tn: 174405.0000 - fn: 34.0000 - accuracy: 0.9584 - precision: 0.0361 - recall: 0.8927 - auc: 0.9669 - val_loss: 0.0944 - val_tp: 74.0000 - val_fp: 1009.0000 - val_tn: 44484.0000 - val_fn: 2.0000 - val_accuracy: 0.9778 - val_precision: 0.0683 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 21/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2329 - tp: 282.0000 - fp: 7369.0000 - tn: 174590.0000 - fn: 35.0000 - accuracy: 0.9594 - precision: 0.0369 - recall: 0.8896 - auc: 0.9657 - val_loss: 0.0942 - val_tp: 74.0000 - val_fp: 1011.0000 - val_tn: 44482.0000 - val_fn: 2.0000 - val_accuracy: 0.9778 - val_precision: 0.0682 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 22/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2317 - tp: 281.0000 - fp: 7563.0000 - tn: 174396.0000 - fn: 36.0000 - accuracy: 0.9583 - precision: 0.0358 - recall: 0.8864 - auc: 0.9658 - val_loss: 0.0936 - val_tp: 74.0000 - val_fp: 993.0000 - val_tn: 44500.0000 - val_fn: 2.0000 - val_accuracy: 0.9782 - val_precision: 0.0694 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 23/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2109 - tp: 284.0000 - fp: 7520.0000 - tn: 174439.0000 - fn: 33.0000 - accuracy: 0.9586 - precision: 0.0364 - recall: 0.8959 - auc: 0.9702 - val_loss: 0.0940 - val_tp: 74.0000 - val_fp: 1003.0000 - val_tn: 44490.0000 - val_fn: 2.0000 - val_accuracy: 0.9779 - val_precision: 0.0687 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 24/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2089 - tp: 283.0000 - fp: 7119.0000 - tn: 174840.0000 - fn: 34.0000 - accuracy: 0.9608 - precision: 0.0382 - recall: 0.8927 - auc: 0.9731 - val_loss: 0.0898 - val_tp: 74.0000 - val_fp: 939.0000 - val_tn: 44554.0000 - val_fn: 2.0000 - val_accuracy: 0.9793 - val_precision: 0.0731 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 25/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2062 - tp: 280.0000 - fp: 7392.0000 - tn: 174567.0000 - fn: 37.0000 - accuracy: 0.9592 - precision: 0.0365 - recall: 0.8833 - auc: 0.9749 - val_loss: 0.0969 - val_tp: 74.0000 - val_fp: 1035.0000 - val_tn: 44458.0000 - val_fn: 2.0000 - val_accuracy: 0.9772 - val_precision: 0.0667 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 26/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2006 - tp: 284.0000 - fp: 7149.0000 - tn: 174810.0000 - fn: 33.0000 - accuracy: 0.9606 - precision: 0.0382 - recall: 0.8959 - auc: 0.9754 - val_loss: 0.0949 - val_tp: 74.0000 - val_fp: 1004.0000 - val_tn: 44489.0000 - val_fn: 2.0000 - val_accuracy: 0.9779 - val_precision: 0.0686 - val_recall: 0.9737 - val_auc: 0.9973
Epoch 27/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2169 - tp: 281.0000 - fp: 7592.0000 - tn: 174367.0000 - fn: 36.0000 - accuracy: 0.9582 - precision: 0.0357 - recall: 0.8864 - auc: 0.9727 - val_loss: 0.0969 - val_tp: 74.0000 - val_fp: 1038.0000 - val_tn: 44455.0000 - val_fn: 2.0000 - val_accuracy: 0.9772 - val_precision: 0.0665 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 28/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2110 - tp: 285.0000 - fp: 6957.0000 - tn: 175002.0000 - fn: 32.0000 - accuracy: 0.9617 - precision: 0.0394 - recall: 0.8991 - auc: 0.9697 - val_loss: 0.0922 - val_tp: 74.0000 - val_fp: 988.0000 - val_tn: 44505.0000 - val_fn: 2.0000 - val_accuracy: 0.9783 - val_precision: 0.0697 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 29/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2234 - tp: 280.0000 - fp: 7030.0000 - tn: 174929.0000 - fn: 37.0000 - accuracy: 0.9612 - precision: 0.0383 - recall: 0.8833 - auc: 0.9679 - val_loss: 0.0942 - val_tp: 74.0000 - val_fp: 1024.0000 - val_tn: 44469.0000 - val_fn: 2.0000 - val_accuracy: 0.9775 - val_precision: 0.0674 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 30/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1889 - tp: 288.0000 - fp: 7228.0000 - tn: 174731.0000 - fn: 29.0000 - accuracy: 0.9602 - precision: 0.0383 - recall: 0.9085 - auc: 0.9771 - val_loss: 0.0895 - val_tp: 74.0000 - val_fp: 954.0000 - val_tn: 44539.0000 - val_fn: 2.0000 - val_accuracy: 0.9790 - val_precision: 0.0720 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 31/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1824 - tp: 286.0000 - fp: 6703.0000 - tn: 175256.0000 - fn: 31.0000 - accuracy: 0.9631 - precision: 0.0409 - recall: 0.9022 - auc: 0.9789 - val_loss: 0.0898 - val_tp: 74.0000 - val_fp: 957.0000 - val_tn: 44536.0000 - val_fn: 2.0000 - val_accuracy: 0.9790 - val_precision: 0.0718 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 32/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2014 - tp: 279.0000 - fp: 6918.0000 - tn: 175041.0000 - fn: 38.0000 - accuracy: 0.9618 - precision: 0.0388 - recall: 0.8801 - auc: 0.9756 - val_loss: 0.0933 - val_tp: 74.0000 - val_fp: 993.0000 - val_tn: 44500.0000 - val_fn: 2.0000 - val_accuracy: 0.9782 - val_precision: 0.0694 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 33/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2153 - tp: 283.0000 - fp: 6885.0000 - tn: 175074.0000 - fn: 34.0000 - accuracy: 0.9620 - precision: 0.0395 - recall: 0.8927 - auc: 0.9708 - val_loss: 0.0905 - val_tp: 74.0000 - val_fp: 949.0000 - val_tn: 44544.0000 - val_fn: 2.0000 - val_accuracy: 0.9791 - val_precision: 0.0723 - val_recall: 0.9737 - val_auc: 0.9974
Epoch 34/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1970 - tp: 283.0000 - fp: 6969.0000 - tn: 174990.0000 - fn: 34.0000 - accuracy: 0.9616 - precision: 0.0390 - recall: 0.8927 - auc: 0.9769 - val_loss: 0.0958 - val_tp: 74.0000 - val_fp: 1018.0000 - val_tn: 44475.0000 - val_fn: 2.0000 - val_accuracy: 0.9776 - val_precision: 0.0678 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 35/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1737 - tp: 290.0000 - fp: 7040.0000 - tn: 174919.0000 - fn: 27.0000 - accuracy: 0.9612 - precision: 0.0396 - recall: 0.9148 - auc: 0.9806 - val_loss: 0.0907 - val_tp: 74.0000 - val_fp: 948.0000 - val_tn: 44545.0000 - val_fn: 2.0000 - val_accuracy: 0.9792 - val_precision: 0.0724 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 36/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1906 - tp: 288.0000 - fp: 6663.0000 - tn: 175296.0000 - fn: 29.0000 - accuracy: 0.9633 - precision: 0.0414 - recall: 0.9085 - auc: 0.9764 - val_loss: 0.0872 - val_tp: 74.0000 - val_fp: 904.0000 - val_tn: 44589.0000 - val_fn: 2.0000 - val_accuracy: 0.9801 - val_precision: 0.0757 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 37/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2071 - tp: 284.0000 - fp: 6688.0000 - tn: 175271.0000 - fn: 33.0000 - accuracy: 0.9631 - precision: 0.0407 - recall: 0.8959 - auc: 0.9727 - val_loss: 0.0905 - val_tp: 74.0000 - val_fp: 939.0000 - val_tn: 44554.0000 - val_fn: 2.0000 - val_accuracy: 0.9793 - val_precision: 0.0731 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 38/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1813 - tp: 283.0000 - fp: 6784.0000 - tn: 175175.0000 - fn: 34.0000 - accuracy: 0.9626 - precision: 0.0400 - recall: 0.8927 - auc: 0.9817 - val_loss: 0.0913 - val_tp: 74.0000 - val_fp: 951.0000 - val_tn: 44542.0000 - val_fn: 2.0000 - val_accuracy: 0.9791 - val_precision: 0.0722 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 39/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1897 - tp: 289.0000 - fp: 6872.0000 - tn: 175087.0000 - fn: 28.0000 - accuracy: 0.9621 - precision: 0.0404 - recall: 0.9117 - auc: 0.9767 - val_loss: 0.0916 - val_tp: 74.0000 - val_fp: 959.0000 - val_tn: 44534.0000 - val_fn: 2.0000 - val_accuracy: 0.9789 - val_precision: 0.0716 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 40/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1546 - tp: 295.0000 - fp: 6584.0000 - tn: 175375.0000 - fn: 22.0000 - accuracy: 0.9638 - precision: 0.0429 - recall: 0.9306 - auc: 0.9858 - val_loss: 0.0904 - val_tp: 74.0000 - val_fp: 948.0000 - val_tn: 44545.0000 - val_fn: 2.0000 - val_accuracy: 0.9792 - val_precision: 0.0724 - val_recall: 0.9737 - val_auc: 0.9976
Epoch 41/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2263 - tp: 278.0000 - fp: 6677.0000 - tn: 175282.0000 - fn: 39.0000 - accuracy: 0.9632 - precision: 0.0400 - recall: 0.8770 - auc: 0.9676 - val_loss: 0.0908 - val_tp: 74.0000 - val_fp: 955.0000 - val_tn: 44538.0000 - val_fn: 2.0000 - val_accuracy: 0.9790 - val_precision: 0.0719 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 42/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2033 - tp: 281.0000 - fp: 6777.0000 - tn: 175182.0000 - fn: 36.0000 - accuracy: 0.9626 - precision: 0.0398 - recall: 0.8864 - auc: 0.9756 - val_loss: 0.0953 - val_tp: 74.0000 - val_fp: 1029.0000 - val_tn: 44464.0000 - val_fn: 2.0000 - val_accuracy: 0.9774 - val_precision: 0.0671 - val_recall: 0.9737 - val_auc: 0.9975
Epoch 43/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1865 - tp: 283.0000 - fp: 6824.0000 - tn: 175135.0000 - fn: 34.0000 - accuracy: 0.9624 - precision: 0.0398 - recall: 0.8927 - auc: 0.9792 - val_loss: 0.0945 - val_tp: 74.0000 - val_fp: 1019.0000 - val_tn: 44474.0000 - val_fn: 2.0000 - val_accuracy: 0.9776 - val_precision: 0.0677 - val_recall: 0.9737 - val_auc: 0.9976
Epoch 44/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1857 - tp: 286.0000 - fp: 6655.0000 - tn: 175304.0000 - fn: 31.0000 - accuracy: 0.9633 - precision: 0.0412 - recall: 0.9022 - auc: 0.9781 - val_loss: 0.0927 - val_tp: 74.0000 - val_fp: 975.0000 - val_tn: 44518.0000 - val_fn: 2.0000 - val_accuracy: 0.9786 - val_precision: 0.0705 - val_recall: 0.9737 - val_auc: 0.9977
Epoch 45/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1874 - tp: 282.0000 - fp: 6595.0000 - tn: 175364.0000 - fn: 35.0000 - accuracy: 0.9636 - precision: 0.0410 - recall: 0.8896 - auc: 0.9810 - val_loss: 0.0934 - val_tp: 74.0000 - val_fp: 974.0000 - val_tn: 44519.0000 - val_fn: 2.0000 - val_accuracy: 0.9786 - val_precision: 0.0706 - val_recall: 0.9737 - val_auc: 0.9977
Epoch 46/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1641 - tp: 291.0000 - fp: 6532.0000 - tn: 175427.0000 - fn: 26.0000 - accuracy: 0.9640 - precision: 0.0426 - recall: 0.9180 - auc: 0.9837 - val_loss: 0.0868 - val_tp: 74.0000 - val_fp: 885.0000 - val_tn: 44608.0000 - val_fn: 2.0000 - val_accuracy: 0.9805 - val_precision: 0.0772 - val_recall: 0.9737 - val_auc: 0.9976
Epoch 47/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1909 - tp: 285.0000 - fp: 6662.0000 - tn: 175297.0000 - fn: 32.0000 - accuracy: 0.9633 - precision: 0.0410 - recall: 0.8991 - auc: 0.9780 - val_loss: 0.0874 - val_tp: 74.0000 - val_fp: 899.0000 - val_tn: 44594.0000 - val_fn: 2.0000 - val_accuracy: 0.9802 - val_precision: 0.0761 - val_recall: 0.9737 - val_auc: 0.9978
Epoch 48/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1687 - tp: 287.0000 - fp: 6627.0000 - tn: 175332.0000 - fn: 30.0000 - accuracy: 0.9635 - precision: 0.0415 - recall: 0.9054 - auc: 0.9836 - val_loss: 0.0875 - val_tp: 74.0000 - val_fp: 899.0000 - val_tn: 44594.0000 - val_fn: 2.0000 - val_accuracy: 0.9802 - val_precision: 0.0761 - val_recall: 0.9737 - val_auc: 0.9977
Epoch 49/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1740 - tp: 291.0000 - fp: 6522.0000 - tn: 175437.0000 - fn: 26.0000 - accuracy: 0.9641 - precision: 0.0427 - recall: 0.9180 - auc: 0.9794 - val_loss: 0.0848 - val_tp: 74.0000 - val_fp: 866.0000 - val_tn: 44627.0000 - val_fn: 2.0000 - val_accuracy: 0.9810 - val_precision: 0.0787 - val_recall: 0.9737 - val_auc: 0.9978
Epoch 50/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1773 - tp: 292.0000 - fp: 6370.0000 - tn: 175589.0000 - fn: 25.0000 - accuracy: 0.9649 - precision: 0.0438 - recall: 0.9211 - auc: 0.9785 - val_loss: 0.0794 - val_tp: 74.0000 - val_fp: 791.0000 - val_tn: 44702.0000 - val_fn: 2.0000 - val_accuracy: 0.9826 - val_precision: 0.0855 - val_recall: 0.9737 - val_auc: 0.9978
Epoch 51/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1828 - tp: 289.0000 - fp: 5870.0000 - tn: 176089.0000 - fn: 28.0000 - accuracy: 0.9676 - precision: 0.0469 - recall: 0.9117 - auc: 0.9789 - val_loss: 0.0791 - val_tp: 74.0000 - val_fp: 807.0000 - val_tn: 44686.0000 - val_fn: 2.0000 - val_accuracy: 0.9822 - val_precision: 0.0840 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 52/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1567 - tp: 290.0000 - fp: 5704.0000 - tn: 176255.0000 - fn: 27.0000 - accuracy: 0.9686 - precision: 0.0484 - recall: 0.9148 - auc: 0.9860 - val_loss: 0.0772 - val_tp: 74.0000 - val_fp: 765.0000 - val_tn: 44728.0000 - val_fn: 2.0000 - val_accuracy: 0.9832 - val_precision: 0.0882 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 53/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1752 - tp: 290.0000 - fp: 6278.0000 - tn: 175681.0000 - fn: 27.0000 - accuracy: 0.9654 - precision: 0.0442 - recall: 0.9148 - auc: 0.9808 - val_loss: 0.0854 - val_tp: 74.0000 - val_fp: 873.0000 - val_tn: 44620.0000 - val_fn: 2.0000 - val_accuracy: 0.9808 - val_precision: 0.0781 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 54/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1644 - tp: 289.0000 - fp: 6298.0000 - tn: 175661.0000 - fn: 28.0000 - accuracy: 0.9653 - precision: 0.0439 - recall: 0.9117 - auc: 0.9833 - val_loss: 0.0875 - val_tp: 74.0000 - val_fp: 904.0000 - val_tn: 44589.0000 - val_fn: 2.0000 - val_accuracy: 0.9801 - val_precision: 0.0757 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 55/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1721 - tp: 290.0000 - fp: 6318.0000 - tn: 175641.0000 - fn: 27.0000 - accuracy: 0.9652 - precision: 0.0439 - recall: 0.9148 - auc: 0.9816 - val_loss: 0.0827 - val_tp: 74.0000 - val_fp: 844.0000 - val_tn: 44649.0000 - val_fn: 2.0000 - val_accuracy: 0.9814 - val_precision: 0.0806 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 56/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1841 - tp: 284.0000 - fp: 6052.0000 - tn: 175907.0000 - fn: 33.0000 - accuracy: 0.9666 - precision: 0.0448 - recall: 0.8959 - auc: 0.9798 - val_loss: 0.0872 - val_tp: 74.0000 - val_fp: 911.0000 - val_tn: 44582.0000 - val_fn: 2.0000 - val_accuracy: 0.9800 - val_precision: 0.0751 - val_recall: 0.9737 - val_auc: 0.9977
Epoch 57/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1710 - tp: 289.0000 - fp: 6593.0000 - tn: 175366.0000 - fn: 28.0000 - accuracy: 0.9637 - precision: 0.0420 - recall: 0.9117 - auc: 0.9824 - val_loss: 0.0856 - val_tp: 74.0000 - val_fp: 890.0000 - val_tn: 44603.0000 - val_fn: 2.0000 - val_accuracy: 0.9804 - val_precision: 0.0768 - val_recall: 0.9737 - val_auc: 0.9978
Epoch 58/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1649 - tp: 287.0000 - fp: 6478.0000 - tn: 175481.0000 - fn: 30.0000 - accuracy: 0.9643 - precision: 0.0424 - recall: 0.9054 - auc: 0.9836 - val_loss: 0.0797 - val_tp: 74.0000 - val_fp: 817.0000 - val_tn: 44676.0000 - val_fn: 2.0000 - val_accuracy: 0.9820 - val_precision: 0.0831 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 59/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1752 - tp: 285.0000 - fp: 5981.0000 - tn: 175978.0000 - fn: 32.0000 - accuracy: 0.9670 - precision: 0.0455 - recall: 0.8991 - auc: 0.9827 - val_loss: 0.0813 - val_tp: 74.0000 - val_fp: 842.0000 - val_tn: 44651.0000 - val_fn: 2.0000 - val_accuracy: 0.9815 - val_precision: 0.0808 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 60/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1374 - tp: 298.0000 - fp: 5910.0000 - tn: 176049.0000 - fn: 19.0000 - accuracy: 0.9675 - precision: 0.0480 - recall: 0.9401 - auc: 0.9884 - val_loss: 0.0760 - val_tp: 74.0000 - val_fp: 764.0000 - val_tn: 44729.0000 - val_fn: 2.0000 - val_accuracy: 0.9832 - val_precision: 0.0883 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 61/100
90/90 [==============================] - 1s 6ms/step - loss: 0.1655 - tp: 288.0000 - fp: 5872.0000 - tn: 176087.0000 - fn: 29.0000 - accuracy: 0.9676 - precision: 0.0468 - recall: 0.9085 - auc: 0.9838 - val_loss: 0.0795 - val_tp: 74.0000 - val_fp: 819.0000 - val_tn: 44674.0000 - val_fn: 2.0000 - val_accuracy: 0.9820 - val_precision: 0.0829 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 62/100
90/90 [==============================] - 1s 6ms/step - loss: 0.2024 - tp: 281.0000 - fp: 6087.0000 - tn: 175872.0000 - fn: 36.0000 - accuracy: 0.9664 - precision: 0.0441 - recall: 0.8864 - auc: 0.9758 - val_loss: 0.0841 - val_tp: 74.0000 - val_fp: 872.0000 - val_tn: 44621.0000 - val_fn: 2.0000 - val_accuracy: 0.9808 - val_precision: 0.0782 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 63/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1624 - tp: 288.0000 - fp: 6407.0000 - tn: 175552.0000 - fn: 29.0000 - accuracy: 0.9647 - precision: 0.0430 - recall: 0.9085 - auc: 0.9855 - val_loss: 0.0836 - val_tp: 74.0000 - val_fp: 876.0000 - val_tn: 44617.0000 - val_fn: 2.0000 - val_accuracy: 0.9807 - val_precision: 0.0779 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 64/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1757 - tp: 287.0000 - fp: 6419.0000 - tn: 175540.0000 - fn: 30.0000 - accuracy: 0.9646 - precision: 0.0428 - recall: 0.9054 - auc: 0.9812 - val_loss: 0.0803 - val_tp: 74.0000 - val_fp: 832.0000 - val_tn: 44661.0000 - val_fn: 2.0000 - val_accuracy: 0.9817 - val_precision: 0.0817 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 65/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1609 - tp: 287.0000 - fp: 6008.0000 - tn: 175951.0000 - fn: 30.0000 - accuracy: 0.9669 - precision: 0.0456 - recall: 0.9054 - auc: 0.9854 - val_loss: 0.0805 - val_tp: 74.0000 - val_fp: 838.0000 - val_tn: 44655.0000 - val_fn: 2.0000 - val_accuracy: 0.9816 - val_precision: 0.0811 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 66/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1593 - tp: 290.0000 - fp: 6104.0000 - tn: 175855.0000 - fn: 27.0000 - accuracy: 0.9664 - precision: 0.0454 - recall: 0.9148 - auc: 0.9838 - val_loss: 0.0801 - val_tp: 74.0000 - val_fp: 822.0000 - val_tn: 44671.0000 - val_fn: 2.0000 - val_accuracy: 0.9819 - val_precision: 0.0826 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 67/100
90/90 [==============================] - 1s 8ms/step - loss: 0.1600 - tp: 292.0000 - fp: 5888.0000 - tn: 176071.0000 - fn: 25.0000 - accuracy: 0.9676 - precision: 0.0472 - recall: 0.9211 - auc: 0.9823 - val_loss: 0.0766 - val_tp: 74.0000 - val_fp: 786.0000 - val_tn: 44707.0000 - val_fn: 2.0000 - val_accuracy: 0.9827 - val_precision: 0.0860 - val_recall: 0.9737 - val_auc: 0.9981
Epoch 68/100
90/90 [==============================] - 1s 8ms/step - loss: 0.1803 - tp: 286.0000 - fp: 5871.0000 - tn: 176088.0000 - fn: 31.0000 - accuracy: 0.9676 - precision: 0.0465 - recall: 0.9022 - auc: 0.9792 - val_loss: 0.0785 - val_tp: 74.0000 - val_fp: 788.0000 - val_tn: 44705.0000 - val_fn: 2.0000 - val_accuracy: 0.9827 - val_precision: 0.0858 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 69/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1873 - tp: 284.0000 - fp: 5685.0000 - tn: 176274.0000 - fn: 33.0000 - accuracy: 0.9686 - precision: 0.0476 - recall: 0.8959 - auc: 0.9773 - val_loss: 0.0779 - val_tp: 74.0000 - val_fp: 786.0000 - val_tn: 44707.0000 - val_fn: 2.0000 - val_accuracy: 0.9827 - val_precision: 0.0860 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 70/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1525 - tp: 289.0000 - fp: 6048.0000 - tn: 175911.0000 - fn: 28.0000 - accuracy: 0.9667 - precision: 0.0456 - recall: 0.9117 - auc: 0.9857 - val_loss: 0.0791 - val_tp: 74.0000 - val_fp: 803.0000 - val_tn: 44690.0000 - val_fn: 2.0000 - val_accuracy: 0.9823 - val_precision: 0.0844 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 71/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1971 - tp: 284.0000 - fp: 5980.0000 - tn: 175979.0000 - fn: 33.0000 - accuracy: 0.9670 - precision: 0.0453 - recall: 0.8959 - auc: 0.9753 - val_loss: 0.0811 - val_tp: 74.0000 - val_fp: 837.0000 - val_tn: 44656.0000 - val_fn: 2.0000 - val_accuracy: 0.9816 - val_precision: 0.0812 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 72/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1695 - tp: 286.0000 - fp: 6341.0000 - tn: 175618.0000 - fn: 31.0000 - accuracy: 0.9650 - precision: 0.0432 - recall: 0.9022 - auc: 0.9845 - val_loss: 0.0846 - val_tp: 74.0000 - val_fp: 896.0000 - val_tn: 44597.0000 - val_fn: 2.0000 - val_accuracy: 0.9803 - val_precision: 0.0763 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 73/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1766 - tp: 287.0000 - fp: 6297.0000 - tn: 175662.0000 - fn: 30.0000 - accuracy: 0.9653 - precision: 0.0436 - recall: 0.9054 - auc: 0.9818 - val_loss: 0.0824 - val_tp: 74.0000 - val_fp: 865.0000 - val_tn: 44628.0000 - val_fn: 2.0000 - val_accuracy: 0.9810 - val_precision: 0.0788 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 74/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1623 - tp: 289.0000 - fp: 6086.0000 - tn: 175873.0000 - fn: 28.0000 - accuracy: 0.9665 - precision: 0.0453 - recall: 0.9117 - auc: 0.9851 - val_loss: 0.0805 - val_tp: 74.0000 - val_fp: 829.0000 - val_tn: 44664.0000 - val_fn: 2.0000 - val_accuracy: 0.9818 - val_precision: 0.0819 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 75/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1746 - tp: 286.0000 - fp: 6430.0000 - tn: 175529.0000 - fn: 31.0000 - accuracy: 0.9646 - precision: 0.0426 - recall: 0.9022 - auc: 0.9826 - val_loss: 0.0851 - val_tp: 74.0000 - val_fp: 920.0000 - val_tn: 44573.0000 - val_fn: 2.0000 - val_accuracy: 0.9798 - val_precision: 0.0744 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 76/100
90/90 [==============================] - 1s 7ms/step - loss: 0.1439 - tp: 294.0000 - fp: 6075.0000 - tn: 175884.0000 - fn: 23.0000 - accuracy: 0.9665 - precision: 0.0462 - recall: 0.9274 - auc: 0.9877 - val_loss: 0.0818 - val_tp: 74.0000 - val_fp: 872.0000 - val_tn: 44621.0000 - val_fn: 2.0000 - val_accuracy: 0.9808 - val_precision: 0.0782 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 77/100
82/90 [==========================>...] - ETA: 0s - loss: 0.1624 - tp: 266.0000 - fp: 5807.0000 - tn: 161834.0000 - fn: 29.0000 - accuracy: 0.9652 - precision: 0.0438 - recall: 0.9017 - auc: 0.9853Restoring model weights from the end of the best epoch.
90/90 [==============================] - 1s 7ms/step - loss: 0.1554 - tp: 288.0000 - fp: 6332.0000 - tn: 175627.0000 - fn: 29.0000 - accuracy: 0.9651 - precision: 0.0435 - recall: 0.9085 - auc: 0.9862 - val_loss: 0.0818 - val_tp: 74.0000 - val_fp: 875.0000 - val_tn: 44618.0000 - val_fn: 2.0000 - val_accuracy: 0.9808 - val_precision: 0.0780 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 00077: early stoppingplot_metrics(weighted_history) 
train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)
test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE) weighted_results = weighted_model.evaluate(test_features, test_labels,
batch_size=BATCH_SIZE, verbose=0)
for name, value in zip(weighted_model.metrics_names, weighted_results):
print(name, ': ', value)
print()
plot_cm(test_labels, test_predictions_weighted) loss : 0.07622280716896057
tp : 90.0
fp : 998.0
tn : 55865.0
fn : 9.0
accuracy : 0.982321560382843
precision : 0.08272058516740799
recall : 0.9090909361839294
auc : 0.9769566059112549
Legitimate Transactions Detected (True Negatives): 55865
Legitimate Transactions Incorrectly Detected (False Positives): 998
Fraudulent Transactions Missed (False Negatives): 9
Fraudulent Transactions Detected (True Positives): 90
Total Fraudulent Transactions: 99
Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade-offs between these different types of errors for your application.
plot_roc("Train Baseline", train_labels, train_predictions_baseline, color=colors[0])
plot_roc("Test Baseline", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')
plot_roc("Train Weighted", train_labels, train_predictions_weighted, color=colors[1])
plot_roc("Test Weighted", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')
plt.legend(loc='lower right') <matplotlib.legend.Legend at 0x7fbc8c589080>
A related approach would be to resample the dataset by oversampling the minority class.
pos_features = train_features[bool_train_labels]
neg_features = train_features[~bool_train_labels]
pos_labels = train_labels[bool_train_labels]
neg_labels = train_labels[~bool_train_labels] You can balance the dataset manually by choosing the right number of random indices from the positive examples:
ids = np.arange(len(pos_features))
choices = np.random.choice(ids, len(neg_features))
res_pos_features = pos_features[choices]
res_pos_labels = pos_labels[choices]
res_pos_features.shape (181959, 29)resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)
resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)
order = np.arange(len(resampled_labels))
np.random.shuffle(order)
resampled_features = resampled_features[order]
resampled_labels = resampled_labels[order]
resampled_features.shape (363918, 29)Using tf.data
If you're using tf.data the easiest way to produce balanced examples is to start with a positive and a negative dataset, and merge them. See the tf.data guide for more examples.
BUFFER_SIZE = 100000
def make_ds(features, labels):
ds = tf.data.Dataset.from_tensor_slices((features, labels))#.cache()
ds = ds.shuffle(BUFFER_SIZE).repeat()
return ds
pos_ds = make_ds(pos_features, pos_labels)
neg_ds = make_ds(neg_features, neg_labels) Each dataset provides (feature, label) pairs:
for features, label in pos_ds.take(1):
print("Features:\n", features.numpy())
print()
print("Label: ", label.numpy()) Features:
[-1.72731925 1.77656615 -3.74269876 2.74253414 -1.32668397 -1.34677584
-4.46175762 2.0139002 -2.59309618 -5\. 4.12736453 -5.
0.02274489 -5\. -0.32786349 -5\. -5\. -2.96360886
2.89835815 0.75463714 1.2022707 -0.14114195 -0.95544067 -1.22935903
0.18671861 -0.27928716 3.04376109 0.29779937 -1.45688482]
Label: 1Merge the two together using experimental.sample_from_datasets:
resampled_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])
resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2) for features, label in resampled_ds.take(1):
print(label.numpy().mean()) 0.50537109375To use this dataset, you'll need the number of steps per epoch.
The definition of "epoch" in this case is less clear. Say it's the number of batches required to see each negative example once:
resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)
resampled_steps_per_epoch 278.0Now try training the model with the resampled data set instead of using class weights to see how these methods compare.
Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps.
resampled_model = make_model()
resampled_model.load_weights(initial_weights)
# Reset the bias to zero, since this dataset is balanced.
output_layer = resampled_model.layers[-1]
output_layer.bias.assign([0])
val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()
val_ds = val_ds.batch(BATCH_SIZE).prefetch(2)
resampled_history = resampled_model.fit(
resampled_ds,
epochs=EPOCHS,
steps_per_epoch=resampled_steps_per_epoch,
callbacks=[early_stopping],
validation_data=val_ds) Epoch 1/100
278/278 [==============================] - 7s 25ms/step - loss: 0.4377 - tp: 243925.0000 - fp: 88309.0000 - tn: 253494.0000 - fn: 40578.0000 - accuracy: 0.7942 - precision: 0.7342 - recall: 0.8574 - auc: 0.9062 - val_loss: 0.2355 - val_tp: 75.0000 - val_fp: 1532.0000 - val_tn: 43961.0000 - val_fn: 1.0000 - val_accuracy: 0.9664 - val_precision: 0.0467 - val_recall: 0.9868 - val_auc: 0.9984
Epoch 2/100
278/278 [==============================] - 7s 24ms/step - loss: 0.2055 - tp: 258676.0000 - fp: 18235.0000 - tn: 266800.0000 - fn: 25633.0000 - accuracy: 0.9229 - precision: 0.9341 - recall: 0.9098 - auc: 0.9741 - val_loss: 0.1247 - val_tp: 74.0000 - val_fp: 1074.0000 - val_tn: 44419.0000 - val_fn: 2.0000 - val_accuracy: 0.9764 - val_precision: 0.0645 - val_recall: 0.9737 - val_auc: 0.9981
Epoch 3/100
278/278 [==============================] - 7s 24ms/step - loss: 0.1640 - tp: 263104.0000 - fp: 12413.0000 - tn: 272291.0000 - fn: 21536.0000 - accuracy: 0.9404 - precision: 0.9549 - recall: 0.9243 - auc: 0.9834 - val_loss: 0.0981 - val_tp: 74.0000 - val_fp: 937.0000 - val_tn: 44556.0000 - val_fn: 2.0000 - val_accuracy: 0.9794 - val_precision: 0.0732 - val_recall: 0.9737 - val_auc: 0.9980
Epoch 4/100
278/278 [==============================] - 7s 24ms/step - loss: 0.1417 - tp: 265623.0000 - fp: 10539.0000 - tn: 274390.0000 - fn: 18792.0000 - accuracy: 0.9485 - precision: 0.9618 - recall: 0.9339 - auc: 0.9882 - val_loss: 0.0842 - val_tp: 74.0000 - val_fp: 866.0000 - val_tn: 44627.0000 - val_fn: 2.0000 - val_accuracy: 0.9810 - val_precision: 0.0787 - val_recall: 0.9737 - val_auc: 0.9979
Epoch 5/100
278/278 [==============================] - 7s 24ms/step - loss: 0.1275 - tp: 267112.0000 - fp: 9551.0000 - tn: 275482.0000 - fn: 17199.0000 - accuracy: 0.9530 - precision: 0.9655 - recall: 0.9395 - auc: 0.9909 - val_loss: 0.0745 - val_tp: 74.0000 - val_fp: 822.0000 - val_tn: 44671.0000 - val_fn: 2.0000 - val_accuracy: 0.9819 - val_precision: 0.0826 - val_recall: 0.9737 - val_auc: 0.9978
Epoch 6/100
278/278 [==============================] - 7s 24ms/step - loss: 0.1175 - tp: 268432.0000 - fp: 8918.0000 - tn: 276042.0000 - fn: 15952.0000 - accuracy: 0.9563 - precision: 0.9678 - recall: 0.9439 - auc: 0.9926 - val_loss: 0.0663 - val_tp: 74.0000 - val_fp: 750.0000 - val_tn: 44743.0000 - val_fn: 2.0000 - val_accuracy: 0.9835 - val_precision: 0.0898 - val_recall: 0.9737 - val_auc: 0.9972
Epoch 7/100
278/278 [==============================] - 7s 24ms/step - loss: 0.1088 - tp: 269482.0000 - fp: 8208.0000 - tn: 276504.0000 - fn: 15150.0000 - accuracy: 0.9590 - precision: 0.9704 - recall: 0.9468 - auc: 0.9938 - val_loss: 0.0595 - val_tp: 74.0000 - val_fp: 703.0000 - val_tn: 44790.0000 - val_fn: 2.0000 - val_accuracy: 0.9845 - val_precision: 0.0952 - val_recall: 0.9737 - val_auc: 0.9969
Epoch 8/100
278/278 [==============================] - 7s 24ms/step - loss: 0.1040 - tp: 269902.0000 - fp: 7829.0000 - tn: 276891.0000 - fn: 14722.0000 - accuracy: 0.9604 - precision: 0.9718 - recall: 0.9483 - auc: 0.9943 - val_loss: 0.0527 - val_tp: 74.0000 - val_fp: 634.0000 - val_tn: 44859.0000 - val_fn: 2.0000 - val_accuracy: 0.9860 - val_precision: 0.1045 - val_recall: 0.9737 - val_auc: 0.9962
Epoch 9/100
278/278 [==============================] - 7s 24ms/step - loss: 0.0992 - tp: 271117.0000 - fp: 7567.0000 - tn: 276382.0000 - fn: 14278.0000 - accuracy: 0.9616 - precision: 0.9728 - recall: 0.9500 - auc: 0.9948 - val_loss: 0.0509 - val_tp: 74.0000 - val_fp: 651.0000 - val_tn: 44842.0000 - val_fn: 2.0000 - val_accuracy: 0.9857 - val_precision: 0.1021 - val_recall: 0.9737 - val_auc: 0.9959
Epoch 10/100
278/278 [==============================] - 7s 24ms/step - loss: 0.0960 - tp: 270783.0000 - fp: 7337.0000 - tn: 277379.0000 - fn: 13845.0000 - accuracy: 0.9628 - precision: 0.9736 - recall: 0.9514 - auc: 0.9950 - val_loss: 0.0467 - val_tp: 74.0000 - val_fp: 612.0000 - val_tn: 44881.0000 - val_fn: 2.0000 - val_accuracy: 0.9865 - val_precision: 0.1079 - val_recall: 0.9737 - val_auc: 0.9959
Epoch 11/100
278/278 [==============================] - ETA: 0s - loss: 0.0927 - tp: 271368.0000 - fp: 7017.0000 - tn: 277337.0000 - fn: 13622.0000 - accuracy: 0.9637 - precision: 0.9748 - recall: 0.9522 - auc: 0.9954Restoring model weights from the end of the best epoch.
278/278 [==============================] - 7s 25ms/step - loss: 0.0927 - tp: 271368.0000 - fp: 7017.0000 - tn: 277337.0000 - fn: 13622.0000 - accuracy: 0.9637 - precision: 0.9748 - recall: 0.9522 - auc: 0.9954 - val_loss: 0.0434 - val_tp: 74.0000 - val_fp: 555.0000 - val_tn: 44938.0000 - val_fn: 2.0000 - val_accuracy: 0.9878 - val_precision: 0.1176 - val_recall: 0.9737 - val_auc: 0.9957
Epoch 00011: early stoppingIf the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.
But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight.
This smoother gradient signal makes it easier to train the model.
Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data.
plot_metrics(resampled_history) 
Because training is easier on the balanced data, the above training procedure may overfit quickly.
So break up the epochs to give the callbacks.EarlyStopping finer control over when to stop training.
resampled_model = make_model()
resampled_model.load_weights(initial_weights)
# Reset the bias to zero, since this dataset is balanced.
output_layer = resampled_model.layers[-1]
output_layer.bias.assign([0])
resampled_history = resampled_model.fit(
resampled_ds,
# These are not real epochs
steps_per_epoch=20,
epochs=10*EPOCHS,
callbacks=[early_stopping],
validation_data=(val_ds)) Epoch 1/1000
20/20 [==============================] - 1s 56ms/step - loss: 0.9282 - tp: 14181.0000 - fp: 14018.0000 - tn: 51871.0000 - fn: 6459.0000 - accuracy: 0.7634 - precision: 0.5029 - recall: 0.6871 - auc: 0.8612 - val_loss: 0.9177 - val_tp: 68.0000 - val_fp: 32432.0000 - val_tn: 13061.0000 - val_fn: 8.0000 - val_accuracy: 0.2881 - val_precision: 0.0021 - val_recall: 0.8947 - val_auc: 0.8450
Epoch 2/1000
20/20 [==============================] - 1s 26ms/step - loss: 0.7143 - tp: 16471.0000 - fp: 12416.0000 - tn: 7841.0000 - fn: 4232.0000 - accuracy: 0.5936 - precision: 0.5702 - recall: 0.7956 - auc: 0.7514 - val_loss: 0.8173 - val_tp: 70.0000 - val_fp: 28250.0000 - val_tn: 17243.0000 - val_fn: 6.0000 - val_accuracy: 0.3799 - val_precision: 0.0025 - val_recall: 0.9211 - val_auc: 0.9056
Epoch 3/1000
20/20 [==============================] - 1s 26ms/step - loss: 0.5979 - tp: 17119.0000 - fp: 11055.0000 - tn: 9528.0000 - fn: 3258.0000 - accuracy: 0.6506 - precision: 0.6076 - recall: 0.8401 - auc: 0.8194 - val_loss: 0.7158 - val_tp: 70.0000 - val_fp: 22377.0000 - val_tn: 23116.0000 - val_fn: 6.0000 - val_accuracy: 0.5088 - val_precision: 0.0031 - val_recall: 0.9211 - val_auc: 0.9283
Epoch 4/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.5265 - tp: 17496.0000 - fp: 9591.0000 - tn: 10955.0000 - fn: 2918.0000 - accuracy: 0.6946 - precision: 0.6459 - recall: 0.8571 - auc: 0.8542 - val_loss: 0.6276 - val_tp: 72.0000 - val_fp: 16226.0000 - val_tn: 29267.0000 - val_fn: 4.0000 - val_accuracy: 0.6438 - val_precision: 0.0044 - val_recall: 0.9474 - val_auc: 0.9576
Epoch 5/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.4682 - tp: 17653.0000 - fp: 8002.0000 - tn: 12532.0000 - fn: 2773.0000 - accuracy: 0.7369 - precision: 0.6881 - recall: 0.8642 - auc: 0.8793 - val_loss: 0.5534 - val_tp: 75.0000 - val_fp: 10889.0000 - val_tn: 34604.0000 - val_fn: 1.0000 - val_accuracy: 0.7610 - val_precision: 0.0068 - val_recall: 0.9868 - val_auc: 0.9848
Epoch 6/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.4174 - tp: 17837.0000 - fp: 6635.0000 - tn: 13890.0000 - fn: 2598.0000 - accuracy: 0.7746 - precision: 0.7289 - recall: 0.8729 - auc: 0.9003 - val_loss: 0.4905 - val_tp: 75.0000 - val_fp: 6493.0000 - val_tn: 39000.0000 - val_fn: 1.0000 - val_accuracy: 0.8575 - val_precision: 0.0114 - val_recall: 0.9868 - val_auc: 0.9922
Epoch 7/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.3787 - tp: 17939.0000 - fp: 5276.0000 - tn: 15251.0000 - fn: 2494.0000 - accuracy: 0.8103 - precision: 0.7727 - recall: 0.8779 - auc: 0.9162 - val_loss: 0.4380 - val_tp: 75.0000 - val_fp: 4314.0000 - val_tn: 41179.0000 - val_fn: 1.0000 - val_accuracy: 0.9053 - val_precision: 0.0171 - val_recall: 0.9868 - val_auc: 0.9954
Epoch 8/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.3537 - tp: 18025.0000 - fp: 4431.0000 - tn: 15994.0000 - fn: 2510.0000 - accuracy: 0.8305 - precision: 0.8027 - recall: 0.8778 - auc: 0.9242 - val_loss: 0.3942 - val_tp: 75.0000 - val_fp: 3206.0000 - val_tn: 42287.0000 - val_fn: 1.0000 - val_accuracy: 0.9296 - val_precision: 0.0229 - val_recall: 0.9868 - val_auc: 0.9972
Epoch 9/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.3290 - tp: 17975.0000 - fp: 3746.0000 - tn: 16823.0000 - fn: 2416.0000 - accuracy: 0.8496 - precision: 0.8275 - recall: 0.8815 - auc: 0.9341 - val_loss: 0.3560 - val_tp: 75.0000 - val_fp: 2593.0000 - val_tn: 42900.0000 - val_fn: 1.0000 - val_accuracy: 0.9431 - val_precision: 0.0281 - val_recall: 0.9868 - val_auc: 0.9979
Epoch 10/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.3121 - tp: 18157.0000 - fp: 3263.0000 - tn: 17154.0000 - fn: 2386.0000 - accuracy: 0.8621 - precision: 0.8477 - recall: 0.8839 - auc: 0.9397 - val_loss: 0.3239 - val_tp: 75.0000 - val_fp: 2212.0000 - val_tn: 43281.0000 - val_fn: 1.0000 - val_accuracy: 0.9514 - val_precision: 0.0328 - val_recall: 0.9868 - val_auc: 0.9982
Epoch 11/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.2934 - tp: 18151.0000 - fp: 2867.0000 - tn: 17641.0000 - fn: 2301.0000 - accuracy: 0.8738 - precision: 0.8636 - recall: 0.8875 - auc: 0.9471 - val_loss: 0.2972 - val_tp: 75.0000 - val_fp: 1970.0000 - val_tn: 43523.0000 - val_fn: 1.0000 - val_accuracy: 0.9567 - val_precision: 0.0367 - val_recall: 0.9868 - val_auc: 0.9983
Epoch 12/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.2765 - tp: 18454.0000 - fp: 2498.0000 - tn: 17838.0000 - fn: 2170.0000 - accuracy: 0.8860 - precision: 0.8808 - recall: 0.8948 - auc: 0.9537 - val_loss: 0.2727 - val_tp: 75.0000 - val_fp: 1763.0000 - val_tn: 43730.0000 - val_fn: 1.0000 - val_accuracy: 0.9613 - val_precision: 0.0408 - val_recall: 0.9868 - val_auc: 0.9984
Epoch 13/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2649 - tp: 18355.0000 - fp: 2240.0000 - tn: 18272.0000 - fn: 2093.0000 - accuracy: 0.8942 - precision: 0.8912 - recall: 0.8976 - auc: 0.9577 - val_loss: 0.2528 - val_tp: 75.0000 - val_fp: 1638.0000 - val_tn: 43855.0000 - val_fn: 1.0000 - val_accuracy: 0.9640 - val_precision: 0.0438 - val_recall: 0.9868 - val_auc: 0.9984
Epoch 14/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.2547 - tp: 18293.0000 - fp: 2013.0000 - tn: 18577.0000 - fn: 2077.0000 - accuracy: 0.9001 - precision: 0.9009 - recall: 0.8980 - auc: 0.9609 - val_loss: 0.2338 - val_tp: 75.0000 - val_fp: 1516.0000 - val_tn: 43977.0000 - val_fn: 1.0000 - val_accuracy: 0.9667 - val_precision: 0.0471 - val_recall: 0.9868 - val_auc: 0.9985
Epoch 15/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2414 - tp: 18414.0000 - fp: 1709.0000 - tn: 18760.0000 - fn: 2077.0000 - accuracy: 0.9076 - precision: 0.9151 - recall: 0.8986 - auc: 0.9641 - val_loss: 0.2187 - val_tp: 75.0000 - val_fp: 1462.0000 - val_tn: 44031.0000 - val_fn: 1.0000 - val_accuracy: 0.9679 - val_precision: 0.0488 - val_recall: 0.9868 - val_auc: 0.9984
Epoch 16/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2386 - tp: 18465.0000 - fp: 1792.0000 - tn: 18740.0000 - fn: 1963.0000 - accuracy: 0.9083 - precision: 0.9115 - recall: 0.9039 - auc: 0.9660 - val_loss: 0.2044 - val_tp: 75.0000 - val_fp: 1383.0000 - val_tn: 44110.0000 - val_fn: 1.0000 - val_accuracy: 0.9696 - val_precision: 0.0514 - val_recall: 0.9868 - val_auc: 0.9984
Epoch 17/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2290 - tp: 18685.0000 - fp: 1578.0000 - tn: 18675.0000 - fn: 2022.0000 - accuracy: 0.9121 - precision: 0.9221 - recall: 0.9024 - auc: 0.9673 - val_loss: 0.1913 - val_tp: 75.0000 - val_fp: 1297.0000 - val_tn: 44196.0000 - val_fn: 1.0000 - val_accuracy: 0.9715 - val_precision: 0.0547 - val_recall: 0.9868 - val_auc: 0.9983
Epoch 18/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2170 - tp: 18526.0000 - fp: 1453.0000 - tn: 19039.0000 - fn: 1942.0000 - accuracy: 0.9171 - precision: 0.9273 - recall: 0.9051 - auc: 0.9714 - val_loss: 0.1812 - val_tp: 75.0000 - val_fp: 1266.0000 - val_tn: 44227.0000 - val_fn: 1.0000 - val_accuracy: 0.9722 - val_precision: 0.0559 - val_recall: 0.9868 - val_auc: 0.9983
Epoch 19/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.2160 - tp: 18362.0000 - fp: 1402.0000 - tn: 19310.0000 - fn: 1886.0000 - accuracy: 0.9197 - precision: 0.9291 - recall: 0.9069 - auc: 0.9715 - val_loss: 0.1716 - val_tp: 75.0000 - val_fp: 1219.0000 - val_tn: 44274.0000 - val_fn: 1.0000 - val_accuracy: 0.9732 - val_precision: 0.0580 - val_recall: 0.9868 - val_auc: 0.9982
Epoch 20/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.2081 - tp: 18722.0000 - fp: 1297.0000 - tn: 19031.0000 - fn: 1910.0000 - accuracy: 0.9217 - precision: 0.9352 - recall: 0.9074 - auc: 0.9735 - val_loss: 0.1633 - val_tp: 75.0000 - val_fp: 1192.0000 - val_tn: 44301.0000 - val_fn: 1.0000 - val_accuracy: 0.9738 - val_precision: 0.0592 - val_recall: 0.9868 - val_auc: 0.9981
Epoch 21/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2041 - tp: 18929.0000 - fp: 1263.0000 - tn: 18884.0000 - fn: 1884.0000 - accuracy: 0.9232 - precision: 0.9375 - recall: 0.9095 - auc: 0.9742 - val_loss: 0.1571 - val_tp: 75.0000 - val_fp: 1187.0000 - val_tn: 44306.0000 - val_fn: 1.0000 - val_accuracy: 0.9739 - val_precision: 0.0594 - val_recall: 0.9868 - val_auc: 0.9980
Epoch 22/1000
20/20 [==============================] - 1s 27ms/step - loss: 0.2017 - tp: 18834.0000 - fp: 1224.0000 - tn: 19064.0000 - fn: 1838.0000 - accuracy: 0.9252 - precision: 0.9390 - recall: 0.9111 - auc: 0.9752 - val_loss: 0.1508 - val_tp: 75.0000 - val_fp: 1162.0000 - val_tn: 44331.0000 - val_fn: 1.0000 - val_accuracy: 0.9745 - val_precision: 0.0606 - val_recall: 0.9868 - val_auc: 0.9980
Epoch 23/1000
20/20 [==============================] - 1s 28ms/step - loss: 0.1951 - tp: 18612.0000 - fp: 1127.0000 - tn: 19424.0000 - fn: 1797.0000 - accuracy: 0.9286 - precision: 0.9429 - recall: 0.9120 - auc: 0.9764 - val_loss: 0.1458 - val_tp: 75.0000 - val_fp: 1158.0000 - val_tn: 44335.0000 - val_fn: 1.0000 - val_accuracy: 0.9746 - val_precision: 0.0608 - val_recall: 0.9868 - val_auc: 0.9979
Epoch 24/1000
18/20 [==========================>...] - ETA: 0s - loss: 0.1945 - tp: 16932.0000 - fp: 1065.0000 - tn: 17301.0000 - fn: 1566.0000 - accuracy: 0.9286 - precision: 0.9408 - recall: 0.9153 - auc: 0.9765Restoring model weights from the end of the best epoch.
20/20 [==============================] - 1s 27ms/step - loss: 0.1943 - tp: 18796.0000 - fp: 1179.0000 - tn: 19226.0000 - fn: 1759.0000 - accuracy: 0.9283 - precision: 0.9410 - recall: 0.9144 - auc: 0.9765 - val_loss: 0.1401 - val_tp: 75.0000 - val_fp: 1136.0000 - val_tn: 44357.0000 - val_fn: 1.0000 - val_accuracy: 0.9750 - val_precision: 0.0619 - val_recall: 0.9868 - val_auc: 0.9979
Epoch 00024: early stoppingplot_metrics(resampled_history) 
train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)
test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE) resampled_results = resampled_model.evaluate(test_features, test_labels,
batch_size=BATCH_SIZE, verbose=0)
for name, value in zip(resampled_model.metrics_names, resampled_results):
print(name, ': ', value)
print()
plot_cm(test_labels, test_predictions_resampled) loss : 0.23386089503765106
tp : 91.0
fp : 1892.0
tn : 54971.0
fn : 8.0
accuracy : 0.9666444063186646
precision : 0.045890066772699356
recall : 0.9191918969154358
auc : 0.977620542049408
Legitimate Transactions Detected (True Negatives): 54971
Legitimate Transactions Incorrectly Detected (False Positives): 1892
Fraudulent Transactions Missed (False Negatives): 8
Fraudulent Transactions Detected (True Positives): 91
Total Fraudulent Transactions: 99
plot_roc("Train Baseline", train_labels, train_predictions_baseline, color=colors[0])
plot_roc("Test Baseline", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')
plot_roc("Train Weighted", train_labels, train_predictions_weighted, color=colors[1])
plot_roc("Test Weighted", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')
plot_roc("Train Resampled", train_labels, train_predictions_resampled, color=colors[2])
plot_roc("Test Resampled", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')
plt.legend(loc='lower right') <matplotlib.legend.Legend at 0x7fbc386785c0>
Imbalanced data classification is an inherently difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors.