counter = 0
aug = do_inference_aug()
for batch in chunker(test_files[0:test_limit], predict_batch_size):
print("Indexes: %i - %i" % (counter, counter + predict_batch_size))
counter += predict_batch_size
X = [aug(image=(cv2.resize(cv2.imread(x), (img_size, img_size))))['image'] for x in batch]
ids_batch = [get_id_from_file_path(x) for x in batch]
# predict_batch_size must be divisible by the number of TPU cores in use
dummy_rows = len(batch) % number_of_tpu_core
if dummy_rows > 0:
for i in range(0, number_of_tpu_core - dummy_rows):
X.append(np.zeros((img_size,img_size,3), dtype=np.float32))
X = np.array(X)
preds_batch = ((model.predict(X).ravel()*model.predict(X[:, ::-1, :, :]).ravel()*model.predict(X[:, ::-1, ::-1, :]).
ravel()*model.predict(X[:, :, ::-1, :]).ravel())**0.25).tolist()
preds += preds_batch
ids += ids_batch
df = pd.DataFrame({'id': ids, 'label': preds[0:-(number_of_tpu_core - dummy_rows)]})
df.to_csv("results_"+str(fold)+".csv", index=False)
print(df.head())
# sum the predicted values
ensemble_preds += np.array(preds[0:-(number_of_tpu_core - dummy_rows)], dtype=np.float)
for i in filenames:
img = tf.keras.preprocessing.image.load_img(img_path + i,
target_size=(224, 224))
x = tf.keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
y = new_model.predict(x)
y_max = y.max(axis=-1)
if y_max > 0.85:
predict = str(y.argmax(axis=-1))
predict = predict.replace("[", "")
predict = predict.replace("]", "")
predict = int(predict)
else:
predict = 4
predictions.append(predict)
labels = (validation_generator.class_indices)
labels = dict((v, k) for k, v in labels.items())
labels[4] = 'None'
predicted = [labels[k] for k in predictions]
results = pd.DataFrame({
"Filename": filenames,
"Predictions": predicted,
"Actual": filenames
})
for i in labels:
results['Actual'] = results['Actual'].str.replace(labels[i] + "/", "")
results['Actual'] = results['Actual'].str.replace(".jpg", "")
name = []
for x in results['Actual']:
e_n = re.sub("\d+", "", x)
model.fit(x=train_images, y=train_category, epochs=15, batch_size=5, verbose=2)
dataset_test = pd.read_csv('dataset/test.csv')
# import the test images
prediction = []
for i in range(len(dataset_test)):
img = image.load_img('dataset/test/' + dataset_test['Image'][i],
target_size=(224, 224))
img = image.img_to_array(img)
img = preprocess_input(img)
img = np.expand_dims(img, axis=0)
pred = model.predict(np.array(img))
category = np.argmax(pred)
category = np.reshape(category, (-1, 1))
prediction.append(labelEncoder.inverse_transform(category))
dataset_test['target'] = prediction
dataset_test.to_csv('dataset/test.csv', index=False)
# reading the actual predication
actual_predication = pd.read_excel('solution.xlsx')
actual = pd.DataFrame([sub.split(",") for sub in actual_predication['data']],
columns=('Image', 'target'))
data_frame = pd.merge(dataset_test, actual, on='Image', how='inner')
correct = data_frame[data_frame['target_x'] == data_frame['target_y']]
print("number of correct predications", len(correct))
print("accuracy", len(correct) / len(dataset_test))
