def predict_vgg():
# VGG16 generators
# Set up generators
test_batches = ImageDataGenerator(
preprocessing_function= \
applications.vgg16.preprocess_input).flow_from_directory(
test2_path,
target_size=(image_size, image_size),
batch_size=test_batch_size,
shuffle=False)
my_model = applications.VGG16(include_top=True, weights='imagenet')
initial_model = Sequential()
for layer in my_model.layers[:22]:
initial_model.add(layer)
initial_model.layers.pop()
for layer in initial_model.layers:
layer.trainable = False
initial_model.add(Dense(1024, activation='relu'))
initial_model.add(Dropout(0.5))
initial_model.add(Dense(7, activation="softmax"))
initial_model.load_weights('modelVGG.h5')
model = initial_model
print(model.summary())
plot_model(model, to_file="VGG16Model.png", show_shapes=True)
test_batches.reset()
test_labels = test_batches.classes
# Make predictions
predictions = model.predict_generator(test_batches,
steps=test_steps,
verbose=1)
# Declare a function for plotting the confusion matrix
def plot_confusion_matrix(cm,
classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
plt.savefig('confusion_matrix_VGG16.png')
cm = confusion_matrix(test_labels, predictions.argmax(axis=1))
cm_plot_labels = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc']
plot_confusion_matrix(cm, cm_plot_labels)
recall = np.diag(cm) / np.sum(cm, axis=1)
precision = np.diag(cm) / np.sum(cm, axis=0)
print("Mean recall " + str(np.mean(recall).item()))
print("Mean precision " + str(np.mean(precision).item()))
print("Mean recall " + str(
recall_score(
test_labels, predictions.argmax(axis=1), average='weighted')))
print("Balanced Accuracy " +
str(balanced_accuracy_score(test_labels, predictions.argmax(
axis=1))))
print("Mean Precision " + str(
precision_score(
test_labels, predictions.argmax(axis=1), average='weighted')))
print("Mean f1 score " + str(
f1_score(test_labels, predictions.argmax(axis=1), average='weighted')))
file = open("VGG16_results.txt", "w+")
file.write("Mean recall " + str(np.mean(recall).item()) + "\n")
file.write("Mean precision " + str(np.mean(precision).item()) + "\n")
file.write("Mean recall " + str(
recall_score(
test_labels, predictions.argmax(axis=1), average='weighted')) +
"\n")
file.write(
"Balanced Accuracy " +
str(balanced_accuracy_score(test_labels, predictions.argmax(axis=1))) +
"\n")
file.write("Mean Precision " + str(
precision_score(
test_labels, predictions.argmax(axis=1), average='weighted')) +
"\n")
file.write("Mean f1 score " + str(
f1_score(test_labels, predictions.argmax(
axis=1), average='weighted')) + "\n")
file.close()
# print("Roc AUC score: " + str(roc_auc_score(test_labels, predictions.argmax(axis=1))))
predicted_class_indices = np.argmax(predictions, axis=1)
labels = train_batches.class_indices
labels = dict((v, k) for k, v in labels.items())
predictions = [labels[k] for k in predicted_class_indices]
akiec_predictions = labels[0]
filenames = test_batches.filenames
results = pd.DataFrame({
"Filename": filenames,
"Vgg_Predictions": predictions
})
results.to_csv("test_prediction_vgg16.csv", index=False)
def predict_densenet121():
# DesneNet generators
test_batches = ImageDataGenerator(
preprocessing_function= \
applications.densenet.preprocess_input).flow_from_directory(
test2_path,
target_size=(image_size, image_size),
batch_size=test_batch_size,
shuffle=False)
input_tensor = Input(shape=(224, 224, 3))
#Loading the model
model = DenseNet121(input_tensor=input_tensor,
weights='imagenet',
include_top=False)
# add a global spatial average pooling layer
x = model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(7, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=model.input, outputs=predictions)
model.load_weights('modelDenseNet121.h5')
test_batches.reset()
test_labels = test_batches.classes
# Make predictions
predictions = model.predict_generator(test_batches,
steps=test_steps,
verbose=1)
# Declare a function for plotting the confusion matrix
def plot_confusion_matrix(cm,
classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
plt.savefig('confusion_matrix_DenseNet121.png')
cm = confusion_matrix(test_labels, predictions.argmax(axis=1))
cm_plot_labels = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc']
plot_confusion_matrix(cm, cm_plot_labels)
recall = np.diag(cm) / np.sum(cm, axis=1)
precision = np.diag(cm) / np.sum(cm, axis=0)
print("Mean recall " + str(np.mean(recall).item()))
print("Mean precision " + str(np.mean(precision).item()))
print("Mean recall " + str(
recall_score(
test_labels, predictions.argmax(axis=1), average='weighted')))
print("Balanced Accuracy " +
str(balanced_accuracy_score(test_labels, predictions.argmax(
axis=1))))
print("Mean Precision " + str(
precision_score(
test_labels, predictions.argmax(axis=1), average='weighted')))
print("Mean f1 score " + str(
f1_score(test_labels, predictions.argmax(axis=1), average='weighted')))
file = open("DenseNet121_results_last", "w+")
file.write("Mean recall " + str(np.mean(recall).item()) + "\n")
file.write("Mean precision " + str(np.mean(precision).item()) + "\n")
file.write("Mean recall " + str(
recall_score(
test_labels, predictions.argmax(axis=1), average='weighted')) +
"\n")
file.write(
"Balanced Accuracy " +
str(balanced_accuracy_score(test_labels, predictions.argmax(axis=1))) +
"\n")
file.write("Mean Precision " + str(
precision_score(
test_labels, predictions.argmax(axis=1), average='weighted')) +
"\n")
file.write("Mean f1 score " + str(
f1_score(test_labels, predictions.argmax(
axis=1), average='weighted')) + "\n")
file.close()
predicted_class_indices = np.argmax(predictions, axis=1)
labels = train_batches.class_indices
labels = dict((v, k) for k, v in labels.items())
predictions = [labels[k] for k in predicted_class_indices]
filenames = test_batches.filenames
results = pd.DataFrame({
"Filename": filenames,
"DenseNet121_Predictions": predictions
})
results.to_csv("test_prediction_densenet121.csv", index=False)
