diff options
| -rw-r--r-- | .DS_Store | bin | 0 -> 6148 bytes | |||
| -rw-r--r-- | __pycache__/hyperparameters.cpython-38.pyc | bin | 0 -> 440 bytes | |||
| -rw-r--r-- | __pycache__/losses.cpython-38.pyc | bin | 0 -> 3369 bytes | |||
| -rw-r--r-- | __pycache__/preprocess.cpython-38.pyc | bin | 0 -> 5048 bytes | |||
| -rw-r--r-- | losses.py | 12 | ||||
| -rw-r--r-- | main.py | 12 |
6 files changed, 12 insertions, 12 deletions
diff --git a/.DS_Store b/.DS_Store Binary files differnew file mode 100644 index 00000000..bba36123 --- /dev/null +++ b/.DS_Store diff --git a/__pycache__/hyperparameters.cpython-38.pyc b/__pycache__/hyperparameters.cpython-38.pyc Binary files differnew file mode 100644 index 00000000..637c2796 --- /dev/null +++ b/__pycache__/hyperparameters.cpython-38.pyc diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc Binary files differnew file mode 100644 index 00000000..398e9cb3 --- /dev/null +++ b/__pycache__/losses.cpython-38.pyc diff --git a/__pycache__/preprocess.cpython-38.pyc b/__pycache__/preprocess.cpython-38.pyc Binary files differnew file mode 100644 index 00000000..e2f42bca --- /dev/null +++ b/__pycache__/preprocess.cpython-38.pyc @@ -63,13 +63,13 @@ class YourModel(tf.keras.Model): # Dense(15, activation='softmax') ] - self.vgg16 = tf.keras.Sequential(self.vgg16, name="vgg_base") - self.head = tf.keras.Sequential(self.head, name="vgg_head") + # self.vgg16 = tf.keras.Sequential(self.vgg16, name="vgg_base") + # self.head = tf.keras.Sequential(self.head, name="vgg_head") - self.indexed_layers = [layer for layer in self.vgg16 if layer.name.contains("conv1")] - self.desired = [layer.name for layer in self.vgg16 if layer.name.contains("conv1")] + self.indexed_layers = [layer for layer in self.vgg16 if layer.name == "conv1"] + self.desired = [layer.name for layer in self.vgg16 if layer.name == "conv1"] - def forward_pass(self, x): + def call(self, x): layers = [] for layer in self.vgg16.layers: # pass the x through @@ -83,7 +83,7 @@ class YourModel(tf.keras.Model): return x, np.array(layers) - def loss_function(self, p, a, x): + def loss_fn(self, p, a, x): _, photo_layers = self.forward_pass(p) _, art_layers = self.forward_pass(a) _, input_layers = self.forward_pass(x) @@ -6,11 +6,11 @@ from datetime import datetime import tensorflow as tf import hyperparameters as hp -from models import YourModel, VGGModel +from losses import YourModel from preprocess import Datasets from skimage.transform import resize -from tensorboard_utils import \ - ImageLabelingLogger, ConfusionMatrixLogger, CustomModelSaver +# from tensorboard_utils import \ +# ImageLabelingLogger, ConfusionMatrixLogger, CustomModelSaver from skimage.io import imread from lime import lime_image @@ -128,9 +128,9 @@ def train(model, datasets, checkpoint_path, logs_path, init_epoch): tf.keras.callbacks.TensorBoard( log_dir=logs_path, update_freq='batch', - profile_batch=0), - ImageLabelingLogger(logs_path, datasets), - CustomModelSaver(checkpoint_path, ARGS.task, hp.max_num_weights) + profile_batch=0) + # ImageLabelingLogger(logs_path, datasets), + # CustomModelSaver(checkpoint_path, ARGS.task, hp.max_num_weights) ] # Include confusion logger in callbacks if flag set |