Row 2110

Row ID: 2110 | Dataset Entry | Axioma AXP Content Repository

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I have a problem where I'm trying to create an AI model that would recognize different car models, currently I have 8 different car models each with about 160 images of cars in their data folders , but every time I try to run the code

hist=model.fit(train,epochs=20,validation_data=val,callbacks=[tensorboard_callback])

I get a loss that is just exponentially rising into a minus

Epoch 1/20 18/18 [==============================] - 16s 790ms/step - loss: -1795.6414 - accuracy: 0.1319 - val_loss: -8472.8076 - val_accuracy: 0.1625 Epoch 2/20 18/18 [==============================] - 14s 718ms/step - loss: -79825.2422 - accuracy: 0.1493 - val_loss: -311502.5625 - val_accuracy: 0.1250 Epoch 3/20 18/18 [==============================] - 14s 720ms/step - loss: -1431768.2500 - accuracy: 0.1337 - val_loss: -3777775.2500 - val_accuracy: 0.1375 Epoch 4/20 18/18 [==============================] - 14s 716ms/step - loss: -11493728.0000 - accuracy: 0.1354 - val_loss: -28981542.0000 - val_accuracy: 0.1312 Epoch 5/20 18/18 [==============================] - 14s 747ms/step - loss: -61516224.0000 - accuracy: 0.1372 - val_loss: -127766784.0000 - val_accuracy: 0.1250 Epoch 6/20 18/18 [==============================] - 14s 719ms/step - loss: -251817104.0000 - accuracy: 0.1302 - val_loss: -401455168.0000 - val_accuracy: 0.1813 Epoch 7/20 18/18 [==============================] - 14s 755ms/step - loss: -731479360.0000 - accuracy: 0.1476 - val_loss: -1354252672.0000 - val_accuracy: 0.1375 Epoch 8/20 18/18 [==============================] - 14s 753ms/step - loss: -2031392128.0000 - accuracy: 0.1354 - val_loss: -3004264448.0000 - val_accuracy: 0.1625 Epoch 9/20 18/18 [==============================] - 14s 711ms/step - loss: -4619375104.0000 - accuracy: 0.1302 - val_loss: -7603259904.0000 - val_accuracy: 0.1125 Epoch 10/20 2/18 [==>...........................] - ETA: 10s - loss: -7608679424.0000 - accuracy: 0.1094

This is the loss function that I am using

model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])

this is my model

model.add(Conv2D(16,(3,3),1,activation='relu',input_shape=(256,256,3))) model.add(MaxPooling2D()) model.add(Conv2D(32,(3,3),1,activation='relu')) model.add(MaxPooling2D()) model.add(Conv2D(16,(3,3),1,activation='relu')) model.add(MaxPooling2D()) model.add(Flatten()) model.add(Dense(256,activation='relu')) model.add(Dense(1,activation='sigmoid'))

I've normalized the data by doing

data=data.map(lambda x,y: (x/255, y))

so the values are from 0 to 1

I've read something online about GPU's so I'm not sure if it's that , I can't find a fix , but I'm using this to speed it up

​

gpus =tf.config.experimental.list_physical_devices('GPU') for gpu in gpus: tf.config.experimental.set_memory_growth(gpu,True)

Any help is welcome!

I'm trying to train a model and get the loss closer to a zero, and accuracy closer to 1, but it's just exponentially driving into minus infinity.

FieldValue
text I have a problem where I'm trying to create an AI model that would recognize different car models, currently I have 8 different car models each with about 160 images of cars in their data folders , but every time I try to run the code hist=model.fit(train,epochs=20,validation_data=val,callbacks=[tensorboard_callback]) I get a loss that is just exponentially rising into a minus Epoch 1/20 18/18 [==============================] - 16s 790ms/step - loss: -1795.6414 - accuracy: 0.131…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-06-25
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url_encoded Z0FBQUFBQm5Lak9FNDhNREtzZGdDN0dIaEl3akFMTHcwMEVIM1ZYNFVISHRfc3dHQ1hoMUVQcVhoOU8xaEFGdllsMTV3RVRHblpKYTEySjVCVjJiNjdDOWZSVTVxR0lUSUhqV3pmX244azRCazhhaWJiWWFyZ3hlS1lTY05RMkJxQnhqWmx2YUNLZDhCRWJLNnFlS0wzS0QyOGJUdENNYUc0TWVCVWJyNzM4ckd3VTJkRnhPQm8yRGp1Z3Bwb29iY0Q1NTRlam4tUVBGbVVnNlNlRHl1bUNCTjFhT0F1S0Rkdz09

Raw Record

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  "text": "I have a problem where I'm trying to create an AI model that would recognize different car models, currently I have 8 different car models each with about 160 images of cars in their data folders , but every time I try to run the code\n\n    hist=model.fit(train,epochs=20,validation_data=val,callbacks=[tensorboard_callback])\n\n I get a loss that is just exponentially rising into a minus \n\n    Epoch 1/20\n    18/18 [==============================] - 16s 790ms/step - loss: -1795.6414 - accuracy: 0.1319 - val_loss: -8472.8076 - val_accuracy: 0.1625\n    Epoch 2/20\n    18/18 [==============================] - 14s 718ms/step - loss: -79825.2422 - accuracy: 0.1493 - val_loss: -311502.5625 - val_accuracy: 0.1250\n    Epoch 3/20\n    18/18 [==============================] - 14s 720ms/step - loss: -1431768.2500 - accuracy: 0.1337 - val_loss: -3777775.2500 - val_accuracy: 0.1375\n    Epoch 4/20\n    18/18 [==============================] - 14s 716ms/step - loss: -11493728.0000 - accuracy: 0.1354 - val_loss: -28981542.0000 - val_accuracy: 0.1312\n    Epoch 5/20\n    18/18 [==============================] - 14s 747ms/step - loss: -61516224.0000 - accuracy: 0.1372 - val_loss: -127766784.0000 - val_accuracy: 0.1250\n    Epoch 6/20\n    18/18 [==============================] - 14s 719ms/step - loss: -251817104.0000 - accuracy: 0.1302 - val_loss: -401455168.0000 - val_accuracy: 0.1813\n    Epoch 7/20\n    18/18 [==============================] - 14s 755ms/step - loss: -731479360.0000 - accuracy: 0.1476 - val_loss: -1354252672.0000 - val_accuracy: 0.1375\n    Epoch 8/20\n    18/18 [==============================] - 14s 753ms/step - loss: -2031392128.0000 - accuracy: 0.1354 - val_loss: -3004264448.0000 - val_accuracy: 0.1625\n    Epoch 9/20\n    18/18 [==============================] - 14s 711ms/step - loss: -4619375104.0000 - accuracy: 0.1302 - val_loss: -7603259904.0000 - val_accuracy: 0.1125\n    Epoch 10/20\n     2/18 [==>...........................] - ETA: 10s - loss: -7608679424.0000 - accuracy: 0.1094\n\n This is the loss function that I am using \n\n    model.compile(optimizer='adam',\n                  loss=tf.keras.losses.BinaryCrossentropy(),\n                  metrics=['accuracy'])\n\n this is my model \n\n    model.add(Conv2D(16,(3,3),1,activation='relu',input_shape=(256,256,3)))\n    model.add(MaxPooling2D())\n    \n    model.add(Conv2D(32,(3,3),1,activation='relu'))\n    model.add(MaxPooling2D())\n    \n    model.add(Conv2D(16,(3,3),1,activation='relu'))\n    model.add(MaxPooling2D())\n    \n    model.add(Flatten())\n    \n    model.add(Dense(256,activation='relu'))\n    model.add(Dense(1,activation='sigmoid'))\n\n I've normalized the data by doing \n\n    data=data.map(lambda x,y: (x/255, y))\n\n \n\nso the values are from 0 to 1\n\nI've read something online about GPU's so I'm not sure if it's that , I can't find a fix , but I'm using this to speed it up\n\n​\n\n    gpus =tf.config.experimental.list_physical_devices('GPU')\n    for gpu in gpus:\n        tf.config.experimental.set_memory_growth(gpu,True)\n\n \n\nAny help is welcome!\n\nI'm trying to train a model and get the loss closer to a zero, and accuracy closer to 1, but it's just exponentially driving into minus infinity.",
  "label": "r/tensorflow",
  "dataType": "post",
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}
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