# High AUC and Accuracy but weird output in confusion matrix

I am working on image classification problem to determine gender given a face. The dataset is located here gender face dataset on kaggle (link to my notebook). The class distribution is as follows.

 Training 1600 images belonging to 2 classes.
Validation 340 images belonging to 2 classes.
Test 340 images belonging to 2 classes.


I am using RESNET along with a few other layers and I am achieving high accuracy.

base = ResNet50(weights='../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
include_top=False,
input_shape=(150,225,3))

inp = Input(shape=(150,225,3))
base_out = base(inp)
out = Flatten()(base_out)
out = Dense(256, activation='relu')(out)
out = Dense(1, activation='sigmoid')(out)
model = Model(inp, out)


I added this callback in order to view the auroc curve.

def auroc(y_true, y_pred):
auc = tf.metrics.auc(y_true, y_pred)[1]
K.get_session().run(tf.local_variables_initializer())
return auc

model.compile(loss='binary_crossentropy',
optimizer=rmsprop,
metrics=['acc',auroc])


My results are ACC: .98 AUC: .9406

The problem is the results of my confusion matrix do not seem to align with the values above. Below is how I am computing the matrix along with the output.

Y_pred = model.predict_generator(test_generator,steps = 340/batch_size)
from sklearn.metrics import confusion_matrix
y_pred = Y_pred > 0.5

con_mat = tf.confusion_matrix(
test_generator.classes,
y_pred,
num_classes=2,
dtype=tf.int32,
name=None,
weights=None
)
with tf.Session():
print('Confusion Matrix: \n\n', tf.Tensor.eval(con_mat,feed_dict=None, session=None))


I am using keras back by tensorflow and ImageDataGenerators to load my data.