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(carnd-term1) carnd@ip-172-31-20-28:~/BC-P3$ python model.py
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
------------------------------\
Parameters\
------------------------------\
learning_rate := 0.0001\
test_size := 0.2\
batch_size := 40\
save_best_only := False\
samples_per_epoch := 20000\
nb_epoch := 30\
data_dir := data\
keep_prob := 0.5\
------------------------------\
____________________________________________________________________________________________________\
Layer (type) Output Shape Param # Connected to\
====================================================================================================\
lambda_1 (Lambda) (None, 66, 200, 3) 0 lambda_input_1[0][0]\
____________________________________________________________________________________________________\
convolution2d_1 (Convolution2D) (None, 31, 98, 24) 1824 lambda_1[0][0]\
____________________________________________________________________________________________________\
convolution2d_2 (Convolution2D) (None, 14, 47, 36) 21636 convolution2d_1[0][0]\
____________________________________________________________________________________________________\
convolution2d_3 (Convolution2D) (None, 5, 22, 48) 43248 convolution2d_2[0][0]\
____________________________________________________________________________________________________\
convolution2d_4 (Convolution2D) (None, 3, 20, 64) 27712 convolution2d_3[0][0]\
____________________________________________________________________________________________________\
convolution2d_5 (Convolution2D) (None, 1, 18, 64) 36928 convolution2d_4[0][0]\
____________________________________________________________________________________________________\
dropout_1 (Dropout) (None, 1, 18, 64) 0 convolution2d_5[0][0]\
____________________________________________________________________________________________________\
flatten_1 (Flatten) (None, 1152) 0 dropout_1[0][0]\
____________________________________________________________________________________________________\
dense_1 (Dense) (None, 100) 115300 flatten_1[0][0]\
____________________________________________________________________________________________________\
dense_2 (Dense) (None, 50) 5050 dense_1[0][0]\
____________________________________________________________________________________________________\
dense_3 (Dense) (None, 10) 510 dense_2[0][0]\
____________________________________________________________________________________________________\
dense_4 (Dense) (None, 1) 11 dense_3[0][0]\
====================================================================================================\
Total params: 252,219\
Trainable params: 252,219\
Non-trainable params: 0\
____________________________________________________________________________________________________\
Epoch 1/30\
/home/carnd/BC-P3/utils.py:100: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 160 but corresponding boolean dimension is 66\
mask[(ym - y1) * (x2 - x1) - (y2 - y1) * (xm - x1) > 0] = 1\
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:\
name: GRID K520\
major: 3 minor: 0 memoryClockRate (GHz) 0.797\
pciBusID 0000:00:03.0\
Total memory: 3.94GiB\
Free memory: 3.91GiB\
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GRID K520, pci bus id: 0000:00:03.0)\
20000/20000 [==============================] - 124s - loss: 0.0336 - val_loss: 0.0129
Epoch 2/30
20000/20000 [==============================] - 114s - loss: 0.0255 - val_loss: 0.0130
Epoch 3/30
20000/20000 [==============================] - 112s - loss: 0.0241 - val_loss: 0.0099
Epoch 4/30
20000/20000 [==============================] - 110s - loss: 0.0234 - val_loss: 0.0105
Epoch 5/30
20000/20000 [==============================] - 110s - loss: 0.0225 - val_loss: 0.0106
Epoch 6/30
20000/20000 [==============================] - 110s - loss: 0.0212 - val_loss: 0.0079
Epoch 7/30
20000/20000 [==============================] - 110s - loss: 0.0209 - val_loss: 0.0093
Epoch 8/30
20000/20000 [==============================] - 110s - loss: 0.0201 - val_loss: 0.0093
Epoch 9/30
20000/20000 [==============================] - 110s - loss: 0.0198 - val_loss: 0.0101
Epoch 10/30
20000/20000 [==============================] - 110s - loss: 0.0198 - val_loss: 0.0114
Epoch 11/30
20000/20000 [==============================] - 110s - loss: 0.0194 - val_loss: 0.0087
Epoch 12/30
20000/20000 [==============================] - 108s - loss: 0.0192 - val_loss: 0.0110
Epoch 13/30
20000/20000 [==============================] - 109s - loss: 0.0193 - val_loss: 0.0078
Epoch 14/30
20000/20000 [==============================] - 110s - loss: 0.0187 - val_loss: 0.0102
Epoch 15/30
20000/20000 [==============================] - 109s - loss: 0.0185 - val_loss: 0.0095
Epoch 16/30
20000/20000 [==============================] - 109s - loss: 0.0180 - val_loss: 0.0101
Epoch 17/30
20000/20000 [==============================] - 109s - loss: 0.0184 - val_loss: 0.0093
Epoch 18/30
20000/20000 [==============================] - 110s - loss: 0.0175 - val_loss: 0.0085
Epoch 19/30
20000/20000 [==============================] - 110s - loss: 0.0174 - val_loss: 0.0090
Epoch 20/30
20000/20000 [==============================] - 110s - loss: 0.0175 - val_loss: 0.0095
Epoch 21/30
20000/20000 [==============================] - 109s - loss: 0.0173 - val_loss: 0.0102
Epoch 22/30
20000/20000 [==============================] - 108s - loss: 0.0169 - val_loss: 0.0103
Epoch 23/30
20000/20000 [==============================] - 109s - loss: 0.0167 - val_loss: 0.0107
Epoch 24/30
20000/20000 [==============================] - 109s - loss: 0.0165 - val_loss: 0.0100
Epoch 25/30
20000/20000 [==============================] - 110s - loss: 0.0167 - val_loss: 0.0109
Epoch 26/30
20000/20000 [==============================] - 109s - loss: 0.0162 - val_loss: 0.0112
Epoch 27/30
20000/20000 [==============================] - 109s - loss: 0.0161 - val_loss: 0.0090
Epoch 28/30
20000/20000 [==============================] - 109s - loss: 0.0162 - val_loss: 0.0094
Epoch 29/30
20000/20000 [==============================] - 108s - loss: 0.0159 - val_loss: 0.0094
Epoch 30/30
20000/20000 [==============================] - 109s - loss: 0.0155 - val_loss: 0.0105