def predict_model(project, weights, user_files):
img_dim = project['img_dim'] * project['img_size']
conv_dim = project['conv_dim'] * project['img_size']
models = []
for weight in project[weights]:
if project['architecture'] == 'resnet50':
models.append(get_resnet_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
elif project['architecture'] == 'xception':
models.append(get_xception_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
else:
models.append(get_inception_v3_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
output = []
user_files = os.path.expanduser(user_files)
if os.path.isdir(user_files):
for aug_gen, file_name in tqdm(gen_from_directory(user_files, img_dim, project)):
predicted, pred_list = multi_predict(aug_gen, models, project['architecture'])
output.append([project[weights], file_name, project['categories'][np.argmax(predicted)]] + pred_list)
elif ((user_files.find('.jpg') > 0) or (user_files.find('.jpeg') > 0) or (user_files.find('.png') > 0)):
aug_gen = prep_from_image(user_files, img_dim, project['augmentations'])
predicted, pred_list = multi_predict(aug_gen, models, project['architecture'])
output.append([project[weights], user_files, project['categories'][np.argmax(predicted)]] + pred_list)
else:
print(colored('Should either be a directory or a .jpg, .jpeg, and .png', 'red'))
return
if len(output) > 0:
columns = ['weights_used','file_name', 'predicted'] + project['categories']
pred_df = pd.DataFrame(output, columns = columns)
predictions_file = os.path.join(project['path'], project['name'] + '_' + weights + '_predictions.csv')
if os.path.isfile(predictions_file):
old_pred_df = pd.read_csv(predictions_file)
pred_df = pd.concat([pred_df, old_pred_df])
os.makedirs(project['path'], exist_ok = True)
pred_df.to_csv(predictions_file, index = False)
print('Predictions saved to:', colored(predictions_file, 'cyan'))
else:
print(colored('No image files found.', 'red'))
def predict_model(project, weights, user_files):
img_dim = project['img_dim'] * project['img_size']
conv_dim = project['conv_dim'] * project['img_size']
models = []
for weight in project[weights]:
if project['architecture'] == 'resnet50':
models.append(get_resnet_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
elif project['architecture'] == 'xception':
models.append(get_xception_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
else:
models.append(get_inception_v3_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
output = []
user_files = os.path.expanduser(user_files)
if os.path.isdir(user_files):
for aug_gen, file_name in tqdm(gen_from_directory(user_files, img_dim, project)):
predicted, pred_list = multi_predict(aug_gen, models, project['architecture'])
output.append([project[weights], file_name, project['categories'][np.argmax(predicted)]] + pred_list)
elif ((user_files.find('.jpg') > 0) or (user_files.find('.jpeg') > 0) or (user_files.find('.png') > 0)):
aug_gen = prep_from_image(user_files, img_dim, project['augmentations'])
predicted, pred_list = multi_predict(aug_gen, models, project['architecture'])
output.append([project[weights], user_files, project['categories'][np.argmax(predicted)]] + pred_list)
else:
print(colored('Should either be a directory or a .jpg, .jpeg, and .png', 'red'))
return
if len(output) > 0:
columns = ['weights_used','file_name', 'predicted'] + project['categories']
pred_df = pd.DataFrame(output, columns = columns)
predictions_file = os.path.join(project['path'], project['name'] + '_' + weights + '_predictions.csv')
if os.path.isfile(predictions_file):
old_pred_df = pd.read_csv(predictions_file)
pred_df = pd.concat([pred_df, old_pred_df])
pred_df.to_csv(predictions_file, index = False)
print('Predictions saved to:', colored(predictions_file, 'cyan'))
else:
print(colored('No image files found.', 'red'))
def train_model(project, final=False, last=False):
weight_label = '-' + project['architecture'] + '-weights-'
source_path = project['path']
weights_path = os.path.join(source_path, 'weights')
plot_path = os.path.join(source_path, 'plots')
if last:
weights = 'last_weights'
else:
weights = 'best_weights'
if final:
weight_label += '-final-'
use_path = os.path.join(source_path, 'augmented')
else:
use_path = os.path.join(source_path, 'pre_model')
project['model_round'] += 1
shutil.rmtree(weights_path, ignore_errors=True)
os.makedirs(weights_path)
shutil.rmtree(plot_path, ignore_errors=True)
os.makedirs(plot_path)
img_dim = project['img_dim'] * project['img_size']
conv_dim = project['conv_dim'] * project['img_size']
lr = project['learning_rate']
decay = project['learning_rate_decay']
all_files = os.listdir(use_path)
pre_model_files = list(filter(lambda x: r'-img-' in x, all_files))
label_names = list(filter(lambda x: r'-label-' in x, all_files))
pre_model_files_df = pd.DataFrame({'files': pre_model_files})
pre_model_files_df['suffix'] = pre_model_files_df.apply(
lambda row: row.files.split('.')[-1], axis=1)
pre_model_files_df = pre_model_files_df[pre_model_files_df.suffix == 'npy']
pre_model_files_df['ind'] = pre_model_files_df.apply(
lambda row: row.files.split('-')[0], axis=1).astype(int)
pre_model_files_df['label'] = pre_model_files_df.apply(
lambda row: row.files.split('-')[3], axis=1)
pre_model_files_df_dedup = pre_model_files_df.drop_duplicates(subset='ind')
pre_model_files_df = pre_model_files_df.set_index(['ind'])
pre_model_files.sort()
label_names.sort()
pre_model_files_arr = np.array(pre_model_files)
label_names_arr = np.array(label_names)
labels = [
np.argmax(np.load(os.path.join(use_path, label_name)))
for label_name in label_names
]
best_weights = []
last_weights = []
if project['kfold'] >= 3:
kfold = StratifiedKFold(n_splits=project['kfold'],
shuffle=True,
random_state=project['seed'])
kfold_generator = kfold.split(pre_model_files_df_dedup,
pre_model_files_df_dedup.label)
validate = True
else:
print('Too few k-folds selected, fitting on all data')
kfold_generator = no_folds_generator(pre_model_files_df_dedup)
validate = False
for i, (train, test) in enumerate(kfold_generator):
if project['kfold_every']:
print('----- Fitting Fold', i, '-----')
elif i > 0:
break
weights_name = project['name'] + weight_label + '-kfold-' + str(
i) + '-round-' + str(project['model_round']) + '.hdf5'
plot_name = project['name'] + weight_label + '-kfold-' + str(
i) + '-round-' + str(project['model_round']) + '.png'
if project[weights] is None:
fold_weights = None
else:
fold_weights = project[weights][i]
if final:
if project['architecture'] == 'resnet50':
model = get_resnet_final_model(img_dim, conv_dim,
project['number_categories'],
fold_weights,
project['is_final'])
elif project['architecture'] == 'xception':
model = get_xception_final_model(img_dim, conv_dim,
project['number_categories'],
fold_weights,
project['is_final'])
else:
model = get_inception_v3_final_model(
img_dim, conv_dim, project['number_categories'],
fold_weights, project['is_final'])
for i, layer in enumerate(model.layers[1].layers):
if len(layer.trainable_weights) > 0:
if i < project['final_cutoff']:
mult = 0.01
else:
mult = 0.1
layer.learning_rate_multiplier = [
mult for tw in layer.trainable_weights
]
else:
if project['architecture'] == 'resnet50':
pre_model, model = get_resnet_pre_post_model(
img_dim,
conv_dim,
len(project['categories']),
model_weights=fold_weights)
elif project['architecture'] == 'xception':
pre_model, model = get_xception_pre_post_model(
img_dim,
conv_dim,
len(project['categories']),
model_weights=fold_weights)
else:
pre_model, model = get_inception_v3_pre_post_model(
img_dim,
conv_dim,
len(project['categories']),
model_weights=fold_weights)
pre_model_files_dedup_train = pre_model_files_df_dedup.iloc[train]
train_ind = list(set(pre_model_files_dedup_train.ind))
pre_model_files_train = pre_model_files_df.loc[train_ind]
gen_train = gen_minibatches(use_path,
pre_model_files_train.files,
project['batch_size'],
project['architecture'],
final=final)
number_train_samples = len(pre_model_files_train)
if validate:
pre_model_files_dedup_test = pre_model_files_df_dedup.iloc[test]
test_ind = list(set(pre_model_files_dedup_test.ind))
pre_model_files_test = pre_model_files_df.loc[test_ind]
gen_test = gen_minibatches(use_path,
pre_model_files_test.files,
project['batch_size'],
project['architecture'],
final=final)
number_test_samples = len(pre_model_files_test)
validation_steps = (number_test_samples // project['batch_size'])
weights_checkpoint_file = weights_name.split(
'.'
)[0] + '-kfold-' + str(
i
) + "-improvement-{epoch:02d}-{val_categorical_accuracy:.4f}.hdf5"
checkpoint = ModelCheckpoint(os.path.join(weights_path,
weights_checkpoint_file),
monitor='val_categorical_accuracy',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
else:
gen_test = None
validation_steps = None
callbacks_list = None
steps_per_epoch = (number_train_samples // project['batch_size'])
for j in range(project['rounds']):
optimizer = Adam(lr=lr, decay=decay)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
model.fit_generator(gen_train,
steps_per_epoch=steps_per_epoch,
epochs=project['cycle'] * (j + 1),
verbose=1,
validation_data=gen_test,
validation_steps=validation_steps,
initial_epoch=j * project['cycle'],
callbacks=callbacks_list)
model.save_weights(os.path.join(weights_path, weights_name))
last_weights.append(os.path.join(weights_path, weights_name))
weights_names = os.listdir(weights_path)
max_val = -1
max_i = -1
for j, name in enumerate(weights_names):
if name.find(weights_name.split('.')[0]) >= 0:
if (name.find(weight_label) >= 0) and (name.find('improvement')
>= 0):
val = int(name.split('.')[1])
if val > max_val:
max_val = val
max_i = j
if project['plot']:
print('Plotting confusion matrix')
if max_i == -1:
print('Loading last weights:',
os.path.join(weights_path, weights_name))
model.load_weights(os.path.join(weights_path, weights_name))
else:
print('Loading best weights:',
os.path.join(weights_path, weights_names[max_i]))
model.load_weights(
os.path.join(weights_path, weights_names[max_i]))
best_predictions = []
true_labels = []
print('Predicting test files')
if validate:
use_files = pre_model_files_test.files
else:
use_files = pre_model_files_train.files
for array_name in tqdm(use_files):
img_path = os.path.join(use_path, array_name)
img = np.load(img_path)
if final:
if project['architecture'] == 'resnet50':
img = np.squeeze(
resnet_preprocess_input(img[np.newaxis].astype(
np.float32)))
elif project['architecture'] == 'xception':
img = np.squeeze(
xception_preprocess_input(img[np.newaxis].astype(
np.float32)))
else:
img = np.squeeze(
inception_v3_preprocess_input(
img[np.newaxis].astype(np.float32)))
prediction = model.predict(img[np.newaxis])
best_predictions.append(
project['categories'][np.argmax(prediction)])
true_label = np.load(img_path.replace('-img-', '-label-'))
true_labels.append(
project['categories'][np.argmax(true_label)])
cm = confusion_matrix(true_labels, best_predictions,
project['categories'])
plt.clf()
sns.heatmap(pd.DataFrame(cm, project['categories'],
project['categories']),
annot=True,
fmt='g')
plt.xlabel('Actual')
plt.xlabel('Predicted')
plt.xticks(rotation=45, fontsize=8)
plt.yticks(rotation=45, fontsize=8)
plt.title('Confusion matrix for fold: ' + str(i) + '\nweights' +
weights_name)
plt.savefig(os.path.join(plot_path, plot_name))
print('Confusion matrix plot saved:',
colored(os.path.join(plot_path, plot_name), 'magenta'))
if max_i == -1:
best_weights.append(os.path.join(weights_path, weights_name))
else:
best_weights.append(
os.path.join(weights_path, weights_names[max_i]))
project['number_categories'] = len(project['categories'])
project['best_weights'] = best_weights
project['last_weights'] = last_weights
project['is_final'] = final
return project
def start_server(project, weights):
app = Flask(__name__)
api = Api(app)
parser = reqparse.RequestParser()
parser.add_argument('img_path', type=str)
img_dim = 224 * project['img_size']
conv_dim = 7 * project['img_size']
models = []
for weight in project[weights]:
if project['architecture'] == 'resnet50':
models.append(
get_resnet_final_model(img_dim, conv_dim,
project['number_categories'], weight,
project['is_final']))
elif project['architecture'] == 'xception':
models.append(
get_xception_final_model(img_dim, conv_dim,
project['number_categories'], weight,
project['is_final']))
else:
models.append(
get_inception_v3_final_model(img_dim, conv_dim,
project['number_categories'],
weight, project['is_final']))
class Predict(Resource):
def post(self):
args = parser.parse_args(strict=True)
img_path = os.path.expanduser(args['img_path'])
if os.path.isfile(img_path):
if img_path.lower().find('.png') > 0 or img_path.lower().find(
'.jpg') > 0 or img_path.lower().find('.jpeg') > 0:
aug_gen = prep_from_image(img_path, img_dim,
project['augmentations'])
pred_list, predicted = multi_predict(
aug_gen, models, project['architecture'])
pred_list = [[float(p) for p in pred]
for pred in list(pred_list)]
result = {
'weights': project[weights],
'image_path': img_path,
'predicted':
project['categories'][np.argmax(predicted)],
'classes': project['categories'],
'class_predictions': pred_list
}
return jsonify(result)
else:
return 'File must be a jpeg or png: ' + args['img_path']
elif os.path.isdir(img_path):
result = []
for aug_gen, file_name in gen_from_directory(
img_path, img_dim, project):
pred_list, predicted = multi_predict(
aug_gen, models, project['architecture'])
pred_list = [[float(p) for p in pred]
for pred in list(pred_list)]
result.append({
'weights':
project[weights],
'image_path':
file_name,
'predicted':
project['categories'][np.argmax(predicted)],
'classes':
project['categories'],
'class_predictions':
pred_list
})
if len(result) > 0:
return jsonify(result)
else:
return 'No images found in directory: ' + args['img_path']
else:
return 'Image does not exist locally: ' + args['img_path']
api.add_resource(Predict, '/predict')
print('')
print('To predict a local image, simply:')
print('')
print(
colored(
'curl http://localhost:' + str(project['api_port']) +
'/predict -d "img_path=/path/to/your/img.png" -X POST', 'green'))
print('')
print('or')
print('')
print(
colored(
'curl http://localhost:' + str(project['api_port']) +
'/predict -d "img_path=/path/to/your/img_dir" -X POST', 'green'))
print('')
app.run(port=str(project['api_port']))
def start_server(project, weights):
app = Flask(__name__)
api = Api(app)
parser = reqparse.RequestParser()
parser.add_argument('img_path', type = str)
img_dim = 224 * project['img_size']
conv_dim = 7 * project['img_size']
models = []
for weight in project[weights]:
if project['architecture'] == 'resnet50':
models.append(get_resnet_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
elif project['architecture'] == 'xception':
models.append(get_xception_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
else:
models.append(get_inception_v3_final_model(img_dim, conv_dim, project['number_categories'], weight, project['is_final']))
class Predict(Resource):
def post(self):
args = parser.parse_args(strict = True)
img_path = os.path.expanduser(args['img_path'])
if os.path.isfile(img_path):
if img_path.lower().find('.png') > 0 or img_path.lower().find('.jpg') > 0 or img_path.lower().find('.jpeg') > 0:
aug_gen = prep_from_image(img_path, img_dim, project['augmentations'])
pred_list, predicted = multi_predict(aug_gen, models, project['architecture'])
pred_list = [[float(p) for p in pred] for pred in list(pred_list)]
result = {'weights': project[weights],
'image_path': img_path,
'predicted': project['categories'][np.argmax(predicted)],
'classes': project['categories'],
'class_predictions': pred_list}
return jsonify(result)
else:
return 'File must be a jpeg or png: ' + args['img_path']
elif os.path.isdir(img_path):
result = []
for aug_gen, file_name in gen_from_directory(img_path, img_dim, project):
pred_list, predicted = multi_predict(aug_gen, models, project['architecture'])
pred_list = [[float(p) for p in pred] for pred in list(pred_list)]
result.append({'weights': project[weights],
'image_path': file_name,
'predicted': project['categories'][np.argmax(predicted)],
'classes': project['categories'],
'class_predictions': pred_list})
if len(result) > 0:
return jsonify(result)
else:
return 'No images found in directory: ' + args['img_path']
else:
return 'Image does not exist locally: ' + args['img_path']
api.add_resource(Predict, '/predict')
print('')
print('To predict a local image, simply:')
print('')
print(colored('curl http://localhost:' + str(project['api_port']) + '/predict -d "img_path=/path/to/your/img.png" -X POST', 'green'))
print('')
print('or')
print('')
print(colored('curl http://localhost:' + str(project['api_port']) + '/predict -d "img_path=/path/to/your/img_dir" -X POST', 'green'))
print('')
app.run(port = project['api_port'])
def train_model(project, final = False, last = False):
weight_label = '-' + project['architecture'] + '-weights-'
source_path = project['path']
weights_path = os.path.join(source_path, 'weights')
plot_path = os.path.join(source_path, 'plots')
if last:
weights = 'last_weights'
else:
weights = 'best_weights'
if final:
weight_label += '-final-'
use_path = os.path.join(source_path, 'augmented')
else:
use_path = os.path.join(source_path, 'pre_model')
project['model_round'] += 1
shutil.rmtree(weights_path,ignore_errors=True)
os.makedirs(plot_path)
img_dim = project['img_dim'] * project['img_size']
conv_dim = project['conv_dim'] * project['img_size']
lr = project['learning_rate']
decay = project['learning_rate_decay']
all_files = os.listdir(use_path)
pre_model_files = list(filter(lambda x: r'-img-' in x, all_files))
label_names = list(filter(lambda x: r'-label-' in x, all_files))
pre_model_files_df = pd.DataFrame({'files': pre_model_files})
pre_model_files_df['suffix'] = pre_model_files_df.apply(lambda row: row.files.split('.')[-1], axis = 1)
pre_model_files_df = pre_model_files_df[pre_model_files_df.suffix == 'npy']
pre_model_files_df['ind'] = pre_model_files_df.apply(lambda row: row.files.split('-')[0], axis = 1).astype(int)
pre_model_files_df['label'] = pre_model_files_df.apply(lambda row: row.files.split('-')[3], axis = 1)
pre_model_files_df_dedup = pre_model_files_df.drop_duplicates(subset='ind')
pre_model_files_df = pre_model_files_df.set_index(['ind'])
pre_model_files.sort()
label_names.sort()
pre_model_files_arr = np.array(pre_model_files)
label_names_arr = np.array(label_names)
labels = [np.argmax(np.load(os.path.join(use_path, label_name))) for label_name in label_names]
best_weights = []
last_weights = []
if project['kfold'] >= 3:
kfold = StratifiedKFold(n_splits=project['kfold'], shuffle=True, random_state = project['seed'])
kfold_generator = kfold.split(pre_model_files_df_dedup, pre_model_files_df_dedup.label)
validate = True
else:
print('Too few k-folds selected, fitting on all data')
kfold_generator = no_folds_generator(pre_model_files_df_dedup)
validate = False
for i, (train, test) in enumerate(kfold_generator):
if project['kfold_every']:
print('----- Fitting Fold', i, '-----')
elif i > 0:
break
weights_name = project['name'] + weight_label + '-kfold-' + str(i) + '-round-' + str(project['model_round']) +'.hdf5'
plot_name = project['name'] + weight_label + '-kfold-' + str(i) + '-round-' + str(project['model_round']) +'.png'
if project[weights] is None:
fold_weights = None
else:
fold_weights = project[weights][i]
if final:
if project['architecture'] == 'resnet50':
model = get_resnet_final_model(img_dim, conv_dim, project['number_categories'], fold_weights, project['is_final'])
elif project['architecture'] == 'xception':
model = get_xception_final_model(img_dim, conv_dim, project['number_categories'], fold_weights, project['is_final'])
else:
model = get_inception_v3_final_model(img_dim, conv_dim, project['number_categories'], fold_weights, project['is_final'])
for i, layer in enumerate(model.layers[1].layers):
if len(layer.trainable_weights) > 0:
if i < project['final_cutoff']:
mult = 0.01
else:
mult = 0.1
layer.learning_rate_multiplier = [mult for tw in layer.trainable_weights]
else:
if project['architecture'] == 'resnet50':
pre_model, model = get_resnet_pre_post_model(img_dim,
conv_dim,
len(project['categories']),
model_weights = fold_weights)
elif project['architecture'] == 'xception':
pre_model, model = get_xception_pre_post_model(img_dim,
conv_dim,
len(project['categories']),
model_weights = fold_weights)
else:
pre_model, model = get_inception_v3_pre_post_model(img_dim,
conv_dim,
len(project['categories']),
model_weights = fold_weights)
pre_model_files_dedup_train = pre_model_files_df_dedup.iloc[train]
train_ind = list(set(pre_model_files_dedup_train.ind))
pre_model_files_train = pre_model_files_df.loc[train_ind]
gen_train = gen_minibatches(use_path, pre_model_files_train.files, project['batch_size'], project['architecture'], final = final)
number_train_samples = len(pre_model_files_train)
if validate:
pre_model_files_dedup_test = pre_model_files_df_dedup.iloc[test]
test_ind = list(set(pre_model_files_dedup_test.ind))
pre_model_files_test = pre_model_files_df.loc[test_ind]
gen_test = gen_minibatches(use_path, pre_model_files_test.files, project['batch_size'], project['architecture'], final = final)
number_test_samples = len(pre_model_files_test)
validation_steps = (number_test_samples // project['batch_size'])
weights_checkpoint_file = weights_name.split('.')[0] + '-kfold-' + str(i) + "-improvement-{epoch:02d}-{val_categorical_accuracy:.4f}.hdf5"
checkpoint = ModelCheckpoint(os.path.join(weights_path, weights_checkpoint_file),
monitor='val_categorical_accuracy',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
else:
gen_test = None
validation_steps = None
callbacks_list = None
steps_per_epoch = (number_train_samples // project['batch_size'])
for j in range(project['rounds']):
optimizer = Adam(lr = lr, decay = decay)
model.compile(optimizer = optimizer,
loss = 'categorical_crossentropy',
metrics = ['categorical_accuracy'])
model.fit_generator(gen_train,
steps_per_epoch = steps_per_epoch,
epochs = project['cycle'] * (j + 1),
verbose = 1,
validation_data = gen_test,
validation_steps = validation_steps,
initial_epoch = j * project['cycle'],
callbacks = callbacks_list)
model.save_weights(os.path.join(weights_path, weights_name))
last_weights.append(os.path.join(weights_path, weights_name))
weights_names = os.listdir(weights_path)
max_val = -1
max_i = -1
for j, name in enumerate(weights_names):
if name.find(weights_name.split('.')[0]) >= 0:
if (name.find(weight_label) >= 0) and (name.find('improvement') >= 0):
val = int(name.split('.')[1])
if val > max_val:
max_val = val
max_i = j
if project['plot']:
print('Plotting confusion matrix')
if max_i == -1:
print('Loading last weights:', os.path.join(weights_path, weights_name))
model.load_weights(os.path.join(weights_path, weights_name))
else:
print('Loading best weights:', os.path.join(weights_path, weights_names[max_i]))
model.load_weights(os.path.join(weights_path, weights_names[max_i]))
best_predictions = []
true_labels = []
print('Predicting test files')
if validate:
use_files = pre_model_files_test.files
else:
use_files = pre_model_files_train.files
for array_name in tqdm(use_files):
img_path = os.path.join(use_path, array_name)
img = np.load(img_path)
if final:
if project['architecture'] == 'resnet50':
img = np.squeeze(resnet_preprocess_input(img[np.newaxis].astype(np.float32)))
elif project['architecture'] == 'xception':
img = np.squeeze(xception_preprocess_input(img[np.newaxis].astype(np.float32)))
else:
img = np.squeeze(inception_v3_preprocess_input(img[np.newaxis].astype(np.float32)))
prediction = model.predict(img[np.newaxis])
best_predictions.append(project['categories'][np.argmax(prediction)])
true_label = np.load(img_path.replace('-img-','-label-'))
true_labels.append(project['categories'][np.argmax(true_label)])
cm = confusion_matrix(true_labels, best_predictions, project['categories'])
plt.clf()
sns.heatmap(pd.DataFrame(cm, project['categories'], project['categories']), annot = True, fmt = 'g')
plt.xlabel('Actual')
plt.xlabel('Predicted')
plt.xticks(rotation = 45, fontsize = 8)
plt.yticks(rotation = 45, fontsize = 8)
plt.title('Confusion matrix for fold: ' + str(i) + '\nweights' + weights_name)
plt.savefig(os.path.join(plot_path, plot_name))
print('Confusion matrix plot saved:', colored(os.path.join(plot_path, plot_name), 'magenta'))
if max_i == -1:
best_weights.append(os.path.join(weights_path, weights_name))
else:
best_weights.append(os.path.join(weights_path, weights_names[max_i]))
project['number_categories'] = len(project['categories'])
project['best_weights'] = best_weights
project['last_weights'] = last_weights
project['is_final'] = final
return project