included option for variable range of images per listing
This commit is contained in:
scott
parent
19723c0ea5
commit
2e10be4469
@ -39,30 +39,24 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
|
||||
"\n",
|
||||
" if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
|
||||
" print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
|
||||
" return model\n",
|
||||
"\n",
|
||||
" for layer in model.layers:\n",
|
||||
" for attr in ['kernel_regularizer']:\n",
|
||||
" if hasattr(layer, attr):\n",
|
||||
" setattr(layer, attr, regularizer)\n",
|
||||
"\n",
|
||||
" # When we change the layers attributes, the change only happens in the model config file\n",
|
||||
" model_json = model.to_json()\n",
|
||||
"\n",
|
||||
" # Save the weights before reloading the model.\n",
|
||||
" tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
|
||||
" model.save_weights(tmp_weights_path)\n",
|
||||
"\n",
|
||||
" # load the model from the config\n",
|
||||
" model = tf.keras.models.model_from_json(model_json)\n",
|
||||
" \n",
|
||||
" # Reload the model weights\n",
|
||||
" model.load_weights(tmp_weights_path, by_name=True)\n",
|
||||
" return model"
|
||||
"def dict_pics_jup():\n",
|
||||
" '''\n",
|
||||
" {source:target} dict used to replace source urls with image location as input\n",
|
||||
" '''\n",
|
||||
" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
|
||||
" with open('temp_pics_source_list.txt') as f:\n",
|
||||
" temp_pics_source_list = json.load(f)\n",
|
||||
" \n",
|
||||
" dict_pics = {}\n",
|
||||
" for k in temp_pics_source_list:\n",
|
||||
" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
|
||||
" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
|
||||
" if patt_1 and patt_2 is not None:\n",
|
||||
" tag = patt_1.group() + patt_2.group().lower()\n",
|
||||
" file_name = target_dir + os.sep + tag\n",
|
||||
" dict_pics.update({k:file_name})\n",
|
||||
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
|
||||
" return dict_pics"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -93,21 +87,33 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def dict_pics():\n",
|
||||
" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
|
||||
" with open('temp_pics_source_list.txt') as f:\n",
|
||||
" temp_pics_source_list = json.load(f)\n",
|
||||
" dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
|
||||
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
|
||||
" return dict_pics\n",
|
||||
"dict_pics = dict_pics_jup()\n",
|
||||
"\n",
|
||||
"with open('women_cat_list.txt') as f:\n",
|
||||
" women_cats = json.load(f)\n",
|
||||
"with open('men_cat_list.txt') as f:\n",
|
||||
" men_cats = json.load(f)\n",
|
||||
" \n",
|
||||
"with open('temp_pics_source_list.txt') as f:\n",
|
||||
" tempics = json.load(f)\n",
|
||||
"# list of image urls that did not get named properly which will be removed from the dataframe\n",
|
||||
"drop_row_vals = []\n",
|
||||
"for pic in tempics:\n",
|
||||
" try:\n",
|
||||
" dict_pics[pic]\n",
|
||||
" except KeyError:\n",
|
||||
" drop_row_vals.append(pic)\n",
|
||||
"\n",
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
|
||||
"df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
|
||||
"\n",
|
||||
"dict_pics = dict_pics()\n",
|
||||
"blah = pd.Series(df.PictureURL)\n",
|
||||
"df = df.drop(labels=['PictureURL'], axis=1)\n",
|
||||
"\n",
|
||||
"blah = blah.apply(lambda x: dict_pics[x])\n",
|
||||
"df = pd.concat([blah, df],axis=1)\n",
|
||||
"df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
|
||||
"# removes non-existent image paths"
|
||||
"df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -117,8 +123,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"\n",
|
||||
"df=df.sample(frac=1)"
|
||||
]
|
||||
},
|
||||
@ -155,16 +159,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Found 4542 validated image filenames belonging to 13 classes.\n",
|
||||
"Found 1135 validated image filenames belonging to 13 classes.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
|
||||
" warnings.warn(\n"
|
||||
"Found 4444 validated image filenames belonging to 7 classes.\n",
|
||||
"Found 1111 validated image filenames belonging to 7 classes.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -2656,12 +2652,12 @@
|
||||
" global_average_pooling2d (Glob (None, 1536) 0 ['conv_7b_ac[0][0]'] \n",
|
||||
" alAveragePooling2D) \n",
|
||||
" \n",
|
||||
" dense (Dense) (None, 13) 19981 ['global_average_pooling2d[0][0]'\n",
|
||||
" dense (Dense) (None, 7) 10759 ['global_average_pooling2d[0][0]'\n",
|
||||
" ] \n",
|
||||
" \n",
|
||||
"==================================================================================================\n",
|
||||
"Total params: 54,356,717\n",
|
||||
"Trainable params: 54,296,173\n",
|
||||
"Total params: 54,347,495\n",
|
||||
"Trainable params: 54,286,951\n",
|
||||
"Non-trainable params: 60,544\n",
|
||||
"__________________________________________________________________________________________________\n"
|
||||
]
|
||||
@ -2671,7 +2667,7 @@
|
||||
"for layer in base_model.layers:\n",
|
||||
" layer.trainable = True\n",
|
||||
" \n",
|
||||
"output = Dense(13, activation='softmax')(base_model.output)\n",
|
||||
"output = Dense(7, activation='softmax')(base_model.output)\n",
|
||||
"model = tf.keras.Model(base_model.input, output)\n",
|
||||
"#model = add_regularization(model)\n",
|
||||
"model.summary()\n"
|
||||
@ -2715,33 +2711,21 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/30\n",
|
||||
"142/142 [==============================] - 71s 426ms/step - loss: 1.5727 - accuracy: 0.4613 - val_loss: 2.5192 - val_accuracy: 0.1233\n",
|
||||
"139/139 [==============================] - 65s 390ms/step - loss: 1.0030 - accuracy: 0.6427 - val_loss: 2.1331 - val_accuracy: 0.1620\n",
|
||||
"Epoch 2/30\n",
|
||||
"142/142 [==============================] - 56s 395ms/step - loss: 0.6991 - accuracy: 0.7644 - val_loss: 1.9969 - val_accuracy: 0.3568\n",
|
||||
"139/139 [==============================] - 51s 365ms/step - loss: 0.3751 - accuracy: 0.8756 - val_loss: 1.7818 - val_accuracy: 0.4707\n",
|
||||
"Epoch 3/30\n",
|
||||
"142/142 [==============================] - 56s 395ms/step - loss: 0.2355 - accuracy: 0.9302 - val_loss: 1.9622 - val_accuracy: 0.4626\n",
|
||||
"139/139 [==============================] - 50s 359ms/step - loss: 0.1193 - accuracy: 0.9622 - val_loss: 1.5616 - val_accuracy: 0.6166\n",
|
||||
"Epoch 4/30\n",
|
||||
"142/142 [==============================] - 56s 393ms/step - loss: 0.1015 - accuracy: 0.9707 - val_loss: 1.8987 - val_accuracy: 0.5207\n",
|
||||
"139/139 [==============================] - 51s 363ms/step - loss: 0.0830 - accuracy: 0.9750 - val_loss: 1.3374 - val_accuracy: 0.6922\n",
|
||||
"Epoch 5/30\n",
|
||||
"142/142 [==============================] - 57s 398ms/step - loss: 0.0899 - accuracy: 0.9736 - val_loss: 1.8865 - val_accuracy: 0.5727\n",
|
||||
"139/139 [==============================] - 50s 360ms/step - loss: 0.0609 - accuracy: 0.9815 - val_loss: 1.3717 - val_accuracy: 0.6976\n",
|
||||
"Epoch 6/30\n",
|
||||
"142/142 [==============================] - 57s 402ms/step - loss: 0.0895 - accuracy: 0.9742 - val_loss: 1.9440 - val_accuracy: 0.5419\n",
|
||||
"139/139 [==============================] - 51s 366ms/step - loss: 0.0593 - accuracy: 0.9818 - val_loss: 1.2600 - val_accuracy: 0.7192\n",
|
||||
"Epoch 7/30\n",
|
||||
"142/142 [==============================] - 58s 406ms/step - loss: 0.0784 - accuracy: 0.9762 - val_loss: 2.2314 - val_accuracy: 0.5145\n",
|
||||
"139/139 [==============================] - 51s 363ms/step - loss: 0.0468 - accuracy: 0.9860 - val_loss: 1.1707 - val_accuracy: 0.7264\n",
|
||||
"Epoch 8/30\n",
|
||||
"142/142 [==============================] - 58s 406ms/step - loss: 0.0536 - accuracy: 0.9852 - val_loss: 2.0704 - val_accuracy: 0.5480\n",
|
||||
"Epoch 9/30\n",
|
||||
"142/142 [==============================] - 57s 399ms/step - loss: 0.0672 - accuracy: 0.9802 - val_loss: 2.3144 - val_accuracy: 0.5189\n",
|
||||
"Epoch 10/30\n",
|
||||
"142/142 [==============================] - 56s 395ms/step - loss: 0.0423 - accuracy: 0.9875 - val_loss: 2.2718 - val_accuracy: 0.5330\n",
|
||||
"Epoch 11/30\n",
|
||||
"142/142 [==============================] - 57s 400ms/step - loss: 0.0325 - accuracy: 0.9903 - val_loss: 2.2889 - val_accuracy: 0.5498\n",
|
||||
"Epoch 12/30\n",
|
||||
"142/142 [==============================] - 56s 396ms/step - loss: 0.0591 - accuracy: 0.9822 - val_loss: 2.1700 - val_accuracy: 0.5577\n",
|
||||
"Epoch 13/30\n",
|
||||
"142/142 [==============================] - 56s 395ms/step - loss: 0.0387 - accuracy: 0.9899 - val_loss: 2.3132 - val_accuracy: 0.5410\n",
|
||||
"Epoch 14/30\n",
|
||||
" 95/142 [===================>..........] - ETA: 14s - loss: 0.0344 - accuracy: 0.9888"
|
||||
" 89/139 [==================>...........] - ETA: 14s - loss: 0.0309 - accuracy: 0.9905"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@ -83,6 +83,33 @@
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def dict_pics_jup():\n",
|
||||
" '''\n",
|
||||
" {source:target} dict used to replace source urls with image location as input\n",
|
||||
" '''\n",
|
||||
" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
|
||||
" with open('temp_pics_source_list.txt') as f:\n",
|
||||
" temp_pics_source_list = json.load(f)\n",
|
||||
" \n",
|
||||
" dict_pics = {}\n",
|
||||
" for k in temp_pics_source_list:\n",
|
||||
" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
|
||||
" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
|
||||
" if patt_1 and patt_2 is not None:\n",
|
||||
" tag = patt_1.group() + patt_2.group().lower()\n",
|
||||
" file_name = target_dir + os.sep + tag\n",
|
||||
" dict_pics.update({k:file_name})\n",
|
||||
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
|
||||
" return dict_pics"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "7a6146e6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
@ -93,31 +120,33 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def dict_pics():\n",
|
||||
" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
|
||||
" with open('temp_pics_source_list.txt') as f:\n",
|
||||
" temp_pics_source_list = json.load(f)\n",
|
||||
" dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
|
||||
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
|
||||
" return dict_pics\n",
|
||||
"dict_pics = dict_pics_jup()\n",
|
||||
"\n",
|
||||
"with open('women_cat_list.txt') as f:\n",
|
||||
" women_cats = json.load(f)\n",
|
||||
"with open('men_cat_list.txt') as f:\n",
|
||||
" men_cats = json.load(f)\n",
|
||||
" \n",
|
||||
"with open('temp_pics_source_list.txt') as f:\n",
|
||||
" tempics = json.load(f)\n",
|
||||
"# list of image urls that did not get named properly which will be removed from the dataframe\n",
|
||||
"drop_row_vals = []\n",
|
||||
"for pic in tempics:\n",
|
||||
" try:\n",
|
||||
" dict_pics[pic]\n",
|
||||
" except KeyError:\n",
|
||||
" drop_row_vals.append(pic)\n",
|
||||
"\n",
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
|
||||
"df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
|
||||
"\n",
|
||||
"dict_pics = dict_pics()\n",
|
||||
"blah = pd.Series(df.PictureURL)\n",
|
||||
"df = df.drop(labels=['PictureURL'], axis=1)\n",
|
||||
"\n",
|
||||
"blah = blah.apply(lambda x: dict_pics[x])\n",
|
||||
"df = pd.concat([blah, df],axis=1)\n",
|
||||
"df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
|
||||
"# removes non-existent image paths"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "7a6146e6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"\n",
|
||||
"df=df.sample(frac=1)"
|
||||
]
|
||||
@ -131,7 +160,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Counter({'11498': 546, '11504': 546, '11505': 546, '11632': 546, '15709': 546, '24087': 546, '45333': 546, '53120': 546, '53548': 546, '53557': 546, '55793': 546, '62107': 546, '95672': 546})\n"
|
||||
"Counter({'11632': 6505, '45333': 6505, '53548': 6505, '53557': 6505, '55793': 6505, '62107': 6505, '95672': 6505})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -163,16 +192,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Found 4542 validated image filenames belonging to 13 classes.\n",
|
||||
"Found 1135 validated image filenames belonging to 13 classes.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
|
||||
" warnings.warn(\n"
|
||||
"Found 29143 validated image filenames belonging to 7 classes.\n",
|
||||
"Found 7285 validated image filenames belonging to 7 classes.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -302,14 +323,14 @@
|
||||
"for layer in base_model.layers[:-1]:\n",
|
||||
" updated_model.add(layer)\n",
|
||||
" if layer.name in ['fc1', 'fc2']:\n",
|
||||
" updated_model.add(Dropout(.75))\n",
|
||||
" updated_model.add(Dropout(.50))\n",
|
||||
"\n",
|
||||
"model = updated_model\n",
|
||||
"\n",
|
||||
"for layer in model.layers:\n",
|
||||
" layer.trainable = True\n",
|
||||
"\n",
|
||||
"model.add(Dense(units=13, activation='softmax'))"
|
||||
"model.add(Dense(units=7, activation='softmax'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -374,11 +395,11 @@
|
||||
" \n",
|
||||
" dropout_1 (Dropout) (None, 4096) 0 \n",
|
||||
" \n",
|
||||
" dense (Dense) (None, 13) 53261 \n",
|
||||
" dense (Dense) (None, 7) 28679 \n",
|
||||
" \n",
|
||||
"=================================================================\n",
|
||||
"Total params: 134,313,805\n",
|
||||
"Trainable params: 134,313,805\n",
|
||||
"Total params: 134,289,223\n",
|
||||
"Trainable params: 134,289,223\n",
|
||||
"Non-trainable params: 0\n",
|
||||
"_________________________________________________________________\n"
|
||||
]
|
||||
@ -402,7 +423,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": null,
|
||||
"id": "9cd2ba27",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
@ -413,77 +434,19 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/30\n",
|
||||
"142/142 [==============================] - 62s 404ms/step - loss: 2.7986 - accuracy: 0.0771 - val_loss: 2.5716 - val_accuracy: 0.0670\n",
|
||||
"911/911 [==============================] - 329s 356ms/step - loss: 1.8477 - accuracy: 0.2577 - val_loss: 1.6306 - val_accuracy: 0.3669\n",
|
||||
"Epoch 2/30\n",
|
||||
"142/142 [==============================] - 53s 370ms/step - loss: 2.6351 - accuracy: 0.0790 - val_loss: 2.5728 - val_accuracy: 0.0802\n",
|
||||
"911/911 [==============================] - 322s 353ms/step - loss: 1.4882 - accuracy: 0.4353 - val_loss: 1.4317 - val_accuracy: 0.4784\n",
|
||||
"Epoch 3/30\n",
|
||||
"142/142 [==============================] - 53s 370ms/step - loss: 2.6157 - accuracy: 0.0852 - val_loss: 2.5786 - val_accuracy: 0.0705\n",
|
||||
"911/911 [==============================] - 323s 354ms/step - loss: 1.3046 - accuracy: 0.5158 - val_loss: 1.2747 - val_accuracy: 0.5235\n",
|
||||
"Epoch 4/30\n",
|
||||
"142/142 [==============================] - 53s 371ms/step - loss: 2.6087 - accuracy: 0.0821 - val_loss: 2.5501 - val_accuracy: 0.1013\n",
|
||||
"911/911 [==============================] - 319s 350ms/step - loss: 1.1691 - accuracy: 0.5681 - val_loss: 1.2090 - val_accuracy: 0.5529\n",
|
||||
"Epoch 5/30\n",
|
||||
"142/142 [==============================] - 53s 371ms/step - loss: 2.5420 - accuracy: 0.1182 - val_loss: 2.4237 - val_accuracy: 0.1401\n",
|
||||
"911/911 [==============================] - 317s 348ms/step - loss: 1.0389 - accuracy: 0.6185 - val_loss: 1.1774 - val_accuracy: 0.5682\n",
|
||||
"Epoch 6/30\n",
|
||||
"142/142 [==============================] - 52s 368ms/step - loss: 2.4275 - accuracy: 0.1640 - val_loss: 2.3050 - val_accuracy: 0.1982\n",
|
||||
"911/911 [==============================] - 317s 348ms/step - loss: 0.9125 - accuracy: 0.6656 - val_loss: 1.2237 - val_accuracy: 0.5639\n",
|
||||
"Epoch 7/30\n",
|
||||
"142/142 [==============================] - 52s 368ms/step - loss: 2.3270 - accuracy: 0.2008 - val_loss: 2.2103 - val_accuracy: 0.2273\n",
|
||||
"Epoch 8/30\n",
|
||||
"142/142 [==============================] - 54s 378ms/step - loss: 2.2011 - accuracy: 0.2294 - val_loss: 2.0607 - val_accuracy: 0.2872\n",
|
||||
"Epoch 9/30\n",
|
||||
"142/142 [==============================] - 54s 378ms/step - loss: 2.0790 - accuracy: 0.2781 - val_loss: 1.9376 - val_accuracy: 0.3419\n",
|
||||
"Epoch 10/30\n",
|
||||
"142/142 [==============================] - 52s 368ms/step - loss: 1.8708 - accuracy: 0.3417 - val_loss: 2.0407 - val_accuracy: 0.3198\n",
|
||||
"Epoch 11/30\n",
|
||||
"142/142 [==============================] - 52s 368ms/step - loss: 1.7588 - accuracy: 0.3743 - val_loss: 1.8968 - val_accuracy: 0.3401\n",
|
||||
"Epoch 12/30\n",
|
||||
"142/142 [==============================] - 52s 369ms/step - loss: 1.5927 - accuracy: 0.4251 - val_loss: 1.8735 - val_accuracy: 0.3542\n",
|
||||
"Epoch 13/30\n",
|
||||
"142/142 [==============================] - 53s 369ms/step - loss: 1.4704 - accuracy: 0.4626 - val_loss: 1.8489 - val_accuracy: 0.3753\n",
|
||||
"Epoch 14/30\n",
|
||||
"142/142 [==============================] - 52s 367ms/step - loss: 1.2452 - accuracy: 0.5484 - val_loss: 1.9595 - val_accuracy: 0.3656\n",
|
||||
"Epoch 15/30\n",
|
||||
"142/142 [==============================] - 52s 370ms/step - loss: 1.1410 - accuracy: 0.5885 - val_loss: 1.9159 - val_accuracy: 0.3930\n",
|
||||
"Epoch 16/30\n",
|
||||
"142/142 [==============================] - 52s 367ms/step - loss: 0.9705 - accuracy: 0.6510 - val_loss: 2.1161 - val_accuracy: 0.3921\n",
|
||||
"Epoch 17/30\n",
|
||||
"142/142 [==============================] - 52s 368ms/step - loss: 0.7992 - accuracy: 0.7041 - val_loss: 2.1271 - val_accuracy: 0.4097\n",
|
||||
"Epoch 18/30\n",
|
||||
"142/142 [==============================] - 52s 369ms/step - loss: 0.7544 - accuracy: 0.7305 - val_loss: 2.0093 - val_accuracy: 0.4361\n",
|
||||
"Epoch 19/30\n",
|
||||
"142/142 [==============================] - 52s 367ms/step - loss: 0.5651 - accuracy: 0.7961 - val_loss: 2.4199 - val_accuracy: 0.3850\n",
|
||||
"Epoch 20/30\n",
|
||||
"142/142 [==============================] - 52s 366ms/step - loss: 0.4566 - accuracy: 0.8342 - val_loss: 2.4058 - val_accuracy: 0.4352\n",
|
||||
"Epoch 21/30\n",
|
||||
"142/142 [==============================] - 52s 364ms/step - loss: 0.3841 - accuracy: 0.8622 - val_loss: 2.5407 - val_accuracy: 0.4141\n",
|
||||
"Epoch 22/30\n",
|
||||
"142/142 [==============================] - 52s 366ms/step - loss: 0.2496 - accuracy: 0.9086 - val_loss: 3.0696 - val_accuracy: 0.4088\n",
|
||||
"Epoch 23/30\n",
|
||||
"142/142 [==============================] - 52s 368ms/step - loss: 0.2847 - accuracy: 0.8985 - val_loss: 2.7929 - val_accuracy: 0.4273\n",
|
||||
"Epoch 24/30\n",
|
||||
"142/142 [==============================] - 52s 369ms/step - loss: 0.2035 - accuracy: 0.9293 - val_loss: 3.0300 - val_accuracy: 0.4132\n",
|
||||
"Epoch 25/30\n",
|
||||
"142/142 [==============================] - ETA: 0s - loss: 0.1872 - accuracy: 0.9377"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-18-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1250\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1251\u001b[0m steps_per_execution=self._steps_per_execution)\n\u001b[0;32m-> 1252\u001b[0;31m val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m 1253\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1254\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1537\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1538\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1539\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 947\u001b[0m \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 950\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mALLOW_DYNAMIC_VARIABLE_CREATION\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 951\u001b[0m raise ValueError(\"Creating variables on a non-first call to a function\"\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3128\u001b[0m (graph_function,\n\u001b[1;32m 3129\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 3131\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1957\u001b[0m and executing_eagerly):\n\u001b[1;32m 1958\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1960\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1961\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 59\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 60\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
"147/911 [===>..........................] - ETA: 3:39 - loss: 0.7312 - accuracy: 0.7256"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@ -94,20 +94,23 @@
|
||||
],
|
||||
"source": [
|
||||
"def dict_pics_jup():\n",
|
||||
" '''\n",
|
||||
" {source:target} dict used to replace source urls with image location as input\n",
|
||||
" '''\n",
|
||||
" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
|
||||
" with open('temp_pics_source_list.txt') as f:\n",
|
||||
" temp_pics_source_list = json.load(f)\n",
|
||||
" dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
|
||||
" \n",
|
||||
" dict_pics = {}\n",
|
||||
" for k in temp_pics_source_list:\n",
|
||||
" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
|
||||
" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
|
||||
" if patt_1 and patt_2 is not None:\n",
|
||||
" tag = patt_1.group() + patt_2.group().lower()\n",
|
||||
" file_name = target_dir + os.sep + tag\n",
|
||||
" dict_pics.update({k:file_name})\n",
|
||||
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
|
||||
" return dict_pics\n",
|
||||
"\n",
|
||||
"dict_pics = dict_pics_jup()\n",
|
||||
"blah = pd.Series(df.PictureURL)\n",
|
||||
"df = df.drop(labels=['PictureURL'], axis=1)\n",
|
||||
"blah = blah.apply(lambda x: dict_pics[x])\n",
|
||||
"df = pd.concat([blah, df],axis=1)\n",
|
||||
"df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
|
||||
"# removes non-existent image paths"
|
||||
" return dict_pics"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -117,7 +120,33 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dict_pics = dict_pics_jup()\n",
|
||||
"\n",
|
||||
"with open('women_cat_list.txt') as f:\n",
|
||||
" women_cats = json.load(f)\n",
|
||||
"with open('men_cat_list.txt') as f:\n",
|
||||
" men_cats = json.load(f)\n",
|
||||
" \n",
|
||||
"with open('temp_pics_source_list.txt') as f:\n",
|
||||
" tempics = json.load(f)\n",
|
||||
"# list of image urls that did not get named properly which will be removed from the dataframe\n",
|
||||
"drop_row_vals = []\n",
|
||||
"for pic in tempics:\n",
|
||||
" try:\n",
|
||||
" dict_pics[pic]\n",
|
||||
" except KeyError:\n",
|
||||
" drop_row_vals.append(pic)\n",
|
||||
"\n",
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
|
||||
"df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
|
||||
"\n",
|
||||
"blah = pd.Series(df.PictureURL)\n",
|
||||
"df = df.drop(labels=['PictureURL'], axis=1)\n",
|
||||
"\n",
|
||||
"blah = blah.apply(lambda x: dict_pics[x])\n",
|
||||
"df = pd.concat([blah, df],axis=1)\n",
|
||||
"df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
|
||||
"\n",
|
||||
"df=df.sample(frac=1)"
|
||||
]
|
||||
@ -304,7 +333,7 @@
|
||||
"#model.add(Dense(64, activation='softmax'))\n",
|
||||
"# model.add(Dropout(.25))\n",
|
||||
"\n",
|
||||
"model.add(Dense(units=13, activation='softmax'))"
|
||||
"model.add(Dense(units=7, activation='softmax'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -36,50 +36,17 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8d94196d",
|
||||
"id": "a5c72863",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
|
||||
"\n",
|
||||
" if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
|
||||
" print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
|
||||
" return model\n",
|
||||
"\n",
|
||||
" for layer in model.layers:\n",
|
||||
" for attr in ['kernel_regularizer']:\n",
|
||||
" if hasattr(layer, attr):\n",
|
||||
" setattr(layer, attr, regularizer)\n",
|
||||
"\n",
|
||||
" # When we change the layers attributes, the change only happens in the model config file\n",
|
||||
" model_json = model.to_json()\n",
|
||||
"\n",
|
||||
" # Save the weights before reloading the model.\n",
|
||||
" tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
|
||||
" model.save_weights(tmp_weights_path)\n",
|
||||
"\n",
|
||||
" # load the model from the config\n",
|
||||
" model = tf.keras.models.model_from_json(model_json)\n",
|
||||
" \n",
|
||||
" # Reload the model weights\n",
|
||||
" model.load_weights(tmp_weights_path, by_name=True)\n",
|
||||
" return model"
|
||||
"#image_faults.faulty_images() # removes faulty images\n",
|
||||
"df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "a5c72863",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"image_faults.faulty_images() # removes faulty images\n",
|
||||
"df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -93,19 +60,23 @@
|
||||
" \n",
|
||||
" dict_pics = {}\n",
|
||||
" for k in temp_pics_source_list:\n",
|
||||
" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
|
||||
" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
|
||||
" if patt_1 and patt_2 is not None:\n",
|
||||
" tag = patt_1.group() + patt_2.group().lower()\n",
|
||||
" file_name = target_dir + os.sep + tag\n",
|
||||
" dict_pics.update({k:file_name})\n",
|
||||
" try:\n",
|
||||
" \n",
|
||||
" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
|
||||
" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
|
||||
" if patt_1 and patt_2 is not None:\n",
|
||||
" tag = patt_1.group() + patt_2.group().lower()\n",
|
||||
" file_name = target_dir + os.sep + tag\n",
|
||||
" dict_pics.update({k:file_name})\n",
|
||||
" except TypeError:\n",
|
||||
" print(k)\n",
|
||||
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
|
||||
" return dict_pics\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 4,
|
||||
"id": "1057a442",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
@ -115,12 +86,19 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"nan\n",
|
||||
"{source:target} dictionary created @ /tf/training_images\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dict_pics = dict_pics_jup()\n",
|
||||
"\n",
|
||||
"with open('women_cat_list.txt') as f:\n",
|
||||
" women_cats = json.load(f)\n",
|
||||
"with open('men_cat_list.txt') as f:\n",
|
||||
" men_cats = json.load(f)\n",
|
||||
" \n",
|
||||
"with open('temp_pics_source_list.txt') as f:\n",
|
||||
" tempics = json.load(f)\n",
|
||||
"# list of image urls that did not get named properly which will be removed from the dataframe\n",
|
||||
@ -130,9 +108,11 @@
|
||||
" dict_pics[pic]\n",
|
||||
" except KeyError:\n",
|
||||
" drop_row_vals.append(pic)\n",
|
||||
" \n",
|
||||
"df = df[df.PictureURL.isin(drop_row_vals)==False]\n",
|
||||
"# TODO drop men's or women's categories here\n",
|
||||
"\n",
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
|
||||
"df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
|
||||
"\n",
|
||||
"blah = pd.Series(df.PictureURL)\n",
|
||||
"df = df.drop(labels=['PictureURL'], axis=1)\n",
|
||||
"\n",
|
||||
@ -143,31 +123,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 5,
|
||||
"id": "7a6146e6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
|
||||
"\n",
|
||||
"df=df.sample(frac=1)\n",
|
||||
"len(drop_row_vals)"
|
||||
"df=df.sample(frac=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 6,
|
||||
"id": "114cc3c0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -179,36 +145,28 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 7,
|
||||
"id": "506aa5cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train, test = train_test_split(train, test_size=0.1, random_state=42)\n",
|
||||
"train, test = train_test_split(train, test_size=0.2, random_state=42)\n",
|
||||
"# stratify=train['PrimaryCategoryID']\n",
|
||||
"# train['PrimaryCategoryID'].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 8,
|
||||
"id": "4d72eb90",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Found 53005 validated image filenames belonging to 13 classes.\n",
|
||||
"Found 13251 validated image filenames belonging to 13 classes.\n"
|
||||
"Found 12276 validated image filenames belonging to 7 classes.\n",
|
||||
"Found 3068 validated image filenames belonging to 7 classes.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -222,6 +180,7 @@
|
||||
" #height_shift_range= 0.2,\n",
|
||||
" #rotation_range= 90,\n",
|
||||
" preprocessing_function=tf.keras.applications.xception.preprocess_input)\n",
|
||||
"\n",
|
||||
"train_generator=datagen.flow_from_dataframe(\n",
|
||||
" dataframe=train[:len(train)],\n",
|
||||
" directory='./training_images',\n",
|
||||
@ -248,7 +207,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 9,
|
||||
"id": "7b70f37f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -258,7 +217,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 10,
|
||||
"id": "1ed54bf5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -275,7 +234,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 11,
|
||||
"id": "85934565",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -286,7 +245,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 12,
|
||||
"id": "6322bcad",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -306,7 +265,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 13,
|
||||
"id": "07fd25c6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -319,7 +278,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 14,
|
||||
"id": "b31af79e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -330,7 +289,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 15,
|
||||
"id": "fe06f2bf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -741,13 +700,13 @@
|
||||
" global_average_pooling2d (Glob (None, 2048) 0 ['block14_sepconv2_act[0][0]'] \n",
|
||||
" alAveragePooling2D) \n",
|
||||
" \n",
|
||||
" dense (Dense) (None, 13) 26637 ['global_average_pooling2d[0][0]'\n",
|
||||
" dense (Dense) (None, 7) 14343 ['global_average_pooling2d[0][0]'\n",
|
||||
" ] \n",
|
||||
" \n",
|
||||
"==================================================================================================\n",
|
||||
"Total params: 20,888,117\n",
|
||||
"Trainable params: 20,833,589\n",
|
||||
"Non-trainable params: 54,528\n",
|
||||
"Total params: 20,875,823\n",
|
||||
"Trainable params: 14,343\n",
|
||||
"Non-trainable params: 20,861,480\n",
|
||||
"__________________________________________________________________________________________________\n"
|
||||
]
|
||||
}
|
||||
@ -756,7 +715,7 @@
|
||||
"for layer in base_model.layers:\n",
|
||||
" layer.trainable = True\n",
|
||||
" \n",
|
||||
"output = Dense(13, activation='softmax')(base_model.output)\n",
|
||||
"output = Dense(7, activation='softmax')(base_model.output)\n",
|
||||
"model = tf.keras.Model(base_model.input, output)\n",
|
||||
"#model = add_regularization(model)\n",
|
||||
"model.summary()\n"
|
||||
@ -764,7 +723,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 16,
|
||||
"id": "ea620129",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -777,7 +736,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 17,
|
||||
"id": "fd5d1246",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -789,7 +748,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 18,
|
||||
"id": "9cd2ba27",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
@ -800,13 +759,7 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/30\n",
|
||||
"829/829 [==============================] - 786s 942ms/step - loss: 1.5037 - accuracy: 0.4896 - val_loss: 1.2946 - val_accuracy: 0.5520\n",
|
||||
"Epoch 2/30\n",
|
||||
"829/829 [==============================] - 726s 875ms/step - loss: 0.8550 - accuracy: 0.7117 - val_loss: 1.3593 - val_accuracy: 0.5593\n",
|
||||
"Epoch 3/30\n",
|
||||
"829/829 [==============================] - 750s 905ms/step - loss: 0.3322 - accuracy: 0.8993 - val_loss: 1.5304 - val_accuracy: 0.5542\n",
|
||||
"Epoch 4/30\n",
|
||||
"172/829 [=====>........................] - ETA: 7:57 - loss: 0.1030 - accuracy: 0.9787"
|
||||
"134/192 [===================>..........] - ETA: 31s - loss: 8.9588 - accuracy: 0.1273"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -816,21 +769,16 @@
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-19-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m<ipython-input-18-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 434\u001b[0m \"\"\"\n\u001b[1;32m 435\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m raise ValueError(\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1032\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1105\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 868\u001b[0m return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 870\u001b[0m expand_composites=expand_composites)\n\u001b[1;32m 871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 868\u001b[0m return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 870\u001b[0m expand_composites=expand_composites)\n\u001b[1;32m 871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1147\u001b[0m \"\"\"\n\u001b[1;32m 1148\u001b[0m \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1150\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1114\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1116\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1117\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1214\u001b[0m _r=1):\n\u001b[1;32m 1215\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1217\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 940\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 941\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 943\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3128\u001b[0m (graph_function,\n\u001b[1;32m 3129\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 3131\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1957\u001b[0m and executing_eagerly):\n\u001b[1;32m 1958\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1960\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1961\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 59\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 60\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
|
||||
36
ebay_api.py
36
ebay_api.py
@ -183,8 +183,6 @@ class ShoppingApi:
|
||||
|
||||
parent_cats = ['3034', '93427'] # Women's and Men's shoe departments
|
||||
cat_list = []
|
||||
# TODO make sep lists for women's and men's shoe cats. Needed to train
|
||||
# mens and women's cats separately. This might improve val. acc. during training
|
||||
|
||||
with open('temp_oauth_token.txt') as f:
|
||||
access_token = json.load(f)
|
||||
@ -408,17 +406,35 @@ class CurateData:
|
||||
'''
|
||||
expand = input("expand image list or use primary listing image? (y or n): ")
|
||||
if ('y' or 'Y') in expand:
|
||||
expanded_class = class_training.explode('PictureURL').reset_index(drop=True)
|
||||
expanded_class = expanded_class.dropna(subset=['PictureURL'])
|
||||
expanded_class = expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
|
||||
count = input('how many images? All [A] or the first <n> images?')
|
||||
if 'A' in count:
|
||||
expanded_class = class_training.explode('PictureURL').reset_index(drop=True)
|
||||
expanded_class = expanded_class.dropna(subset=['PictureURL'])
|
||||
expanded_class = expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
|
||||
|
||||
expanded_dropd = dropd.explode('PictureURL').reset_index(drop=True)
|
||||
expanded_dropd = expanded_dropd.dropna(subset=['PictureURL'])
|
||||
expanded_dropd = expanded_dropd.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
|
||||
expanded_dropd = dropd.explode('PictureURL').reset_index(drop=True)
|
||||
expanded_dropd = expanded_dropd.dropna(subset=['PictureURL'])
|
||||
expanded_dropd = expanded_dropd.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
|
||||
|
||||
expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
|
||||
expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
|
||||
|
||||
temp_pics_source_list = list(set(expanded_class.PictureURL.to_list()))
|
||||
temp_pics_source_list = list(set(expanded_class.PictureURL.to_list()))
|
||||
else:
|
||||
count = int(count)
|
||||
class_training['PictureURL'] = class_training['PictureURL'].apply(lambda x: x[0:count] if len(x)>0 else np.nan)
|
||||
expanded_class = class_training.explode('PictureURL').reset_index(drop=True)
|
||||
expanded_class = expanded_class.dropna(subset=['PictureURL'])
|
||||
expanded_class = expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
|
||||
|
||||
dropd = dropd.dropna(subset=['PictureURL'])
|
||||
dropd['PictureURL'] = dropd['PictureURL'].apply(lambda x: x[0:count] if len(x)>0 else np.nan)
|
||||
expanded_dropd = dropd.explode('PictureURL').reset_index(drop=True)
|
||||
expanded_dropd = expanded_dropd.dropna(subset=['PictureURL'])
|
||||
|
||||
expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
|
||||
|
||||
# retrieves picture URLs from master raw_data.txt and rewrites temp_pics_source_list.txt
|
||||
temp_pics_source_list = list(set(expanded_class.PictureURL.to_list())) # TODO
|
||||
|
||||
else:
|
||||
class_training['PictureURL'] = class_training['PictureURL'].apply(lambda x: x[0] if len(x)>0 else np.nan)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user