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|
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
WARNING:tensorflow:TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
WARNING: TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
WARNING:tensorflow:TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable TF_ALLOW_IOLIBS=1.
WARNING: TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable TF_ALLOW_IOLIBS=1.
WARNING:tensorflow:TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable TF_ALLOW_IOLIBS=1.
WARNING: TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable TF_ALLOW_IOLIBS=1.
/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:150: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
def random_hue(img, max_delta=10.0):
/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:173: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
def random_saturation(img, max_shift):
/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:183: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
def random_contrast(img, center, max_contrast_scale):
/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:192: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
def random_shift(x_img, shift_stddev):
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:44: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:44: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:46: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:46: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.
INFO: Loading experiment spec at /workspace/tao-experiments/spec_files/resnet50/config.txt.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/third_party/keras/tensorflow_backend.py:199: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/third_party/keras/tensorflow_backend.py:199: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.
WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 3, 224, 224) 0
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 64, 112, 112) 9408 input_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 64, 112, 112) 0 conv1[0][0]
__________________________________________________________________________________________________
block_1a_conv_1 (Conv2D) (None, 64, 56, 56) 4096 activation_1[0][0]
__________________________________________________________________________________________________
block_1a_relu_1 (Activation) (None, 64, 56, 56) 0 block_1a_conv_1[0][0]
__________________________________________________________________________________________________
block_1a_conv_2 (Conv2D) (None, 64, 56, 56) 36864 block_1a_relu_1[0][0]
__________________________________________________________________________________________________
block_1a_relu_2 (Activation) (None, 64, 56, 56) 0 block_1a_conv_2[0][0]
__________________________________________________________________________________________________
block_1a_conv_3 (Conv2D) (None, 256, 56, 56) 16384 block_1a_relu_2[0][0]
__________________________________________________________________________________________________
block_1a_conv_shortcut (Conv2D) (None, 256, 56, 56) 16384 activation_1[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 256, 56, 56) 0 block_1a_conv_3[0][0]
block_1a_conv_shortcut[0][0]
__________________________________________________________________________________________________
block_1a_relu (Activation) (None, 256, 56, 56) 0 add_1[0][0]
__________________________________________________________________________________________________
block_1b_conv_1 (Conv2D) (None, 64, 56, 56) 16384 block_1a_relu[0][0]
__________________________________________________________________________________________________
block_1b_relu_1 (Activation) (None, 64, 56, 56) 0 block_1b_conv_1[0][0]
__________________________________________________________________________________________________
block_1b_conv_2 (Conv2D) (None, 64, 56, 56) 36864 block_1b_relu_1[0][0]
__________________________________________________________________________________________________
block_1b_relu_2 (Activation) (None, 64, 56, 56) 0 block_1b_conv_2[0][0]
__________________________________________________________________________________________________
block_1b_conv_3 (Conv2D) (None, 256, 56, 56) 16384 block_1b_relu_2[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 256, 56, 56) 0 block_1b_conv_3[0][0]
block_1a_relu[0][0]
__________________________________________________________________________________________________
block_1b_relu (Activation) (None, 256, 56, 56) 0 add_2[0][0]
__________________________________________________________________________________________________
block_1c_conv_1 (Conv2D) (None, 64, 56, 56) 16384 block_1b_relu[0][0]
__________________________________________________________________________________________________
block_1c_relu_1 (Activation) (None, 64, 56, 56) 0 block_1c_conv_1[0][0]
__________________________________________________________________________________________________
block_1c_conv_2 (Conv2D) (None, 64, 56, 56) 36864 block_1c_relu_1[0][0]
__________________________________________________________________________________________________
block_1c_relu_2 (Activation) (None, 64, 56, 56) 0 block_1c_conv_2[0][0]
__________________________________________________________________________________________________
block_1c_conv_3 (Conv2D) (None, 256, 56, 56) 16384 block_1c_relu_2[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 256, 56, 56) 0 block_1c_conv_3[0][0]
block_1b_relu[0][0]
__________________________________________________________________________________________________
block_1c_relu (Activation) (None, 256, 56, 56) 0 add_3[0][0]
__________________________________________________________________________________________________
block_2a_conv_1 (Conv2D) (None, 128, 28, 28) 32768 block_1c_relu[0][0]
__________________________________________________________________________________________________
block_2a_relu_1 (Activation) (None, 128, 28, 28) 0 block_2a_conv_1[0][0]
__________________________________________________________________________________________________
block_2a_conv_2 (Conv2D) (None, 128, 28, 28) 147456 block_2a_relu_1[0][0]
__________________________________________________________________________________________________
block_2a_relu_2 (Activation) (None, 128, 28, 28) 0 block_2a_conv_2[0][0]
__________________________________________________________________________________________________
block_2a_conv_3 (Conv2D) (None, 512, 28, 28) 65536 block_2a_relu_2[0][0]
__________________________________________________________________________________________________
block_2a_conv_shortcut (Conv2D) (None, 512, 28, 28) 131072 block_1c_relu[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 512, 28, 28) 0 block_2a_conv_3[0][0]
block_2a_conv_shortcut[0][0]
__________________________________________________________________________________________________
block_2a_relu (Activation) (None, 512, 28, 28) 0 add_4[0][0]
__________________________________________________________________________________________________
block_2b_conv_1 (Conv2D) (None, 128, 28, 28) 65536 block_2a_relu[0][0]
__________________________________________________________________________________________________
block_2b_relu_1 (Activation) (None, 128, 28, 28) 0 block_2b_conv_1[0][0]
__________________________________________________________________________________________________
block_2b_conv_2 (Conv2D) (None, 128, 28, 28) 147456 block_2b_relu_1[0][0]
__________________________________________________________________________________________________
block_2b_relu_2 (Activation) (None, 128, 28, 28) 0 block_2b_conv_2[0][0]
__________________________________________________________________________________________________
block_2b_conv_3 (Conv2D) (None, 512, 28, 28) 65536 block_2b_relu_2[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 512, 28, 28) 0 block_2b_conv_3[0][0]
block_2a_relu[0][0]
__________________________________________________________________________________________________
block_2b_relu (Activation) (None, 512, 28, 28) 0 add_5[0][0]
__________________________________________________________________________________________________
block_2c_conv_1 (Conv2D) (None, 128, 28, 28) 65536 block_2b_relu[0][0]
__________________________________________________________________________________________________
block_2c_relu_1 (Activation) (None, 128, 28, 28) 0 block_2c_conv_1[0][0]
__________________________________________________________________________________________________
block_2c_conv_2 (Conv2D) (None, 128, 28, 28) 147456 block_2c_relu_1[0][0]
__________________________________________________________________________________________________
block_2c_relu_2 (Activation) (None, 128, 28, 28) 0 block_2c_conv_2[0][0]
__________________________________________________________________________________________________
block_2c_conv_3 (Conv2D) (None, 512, 28, 28) 65536 block_2c_relu_2[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 512, 28, 28) 0 block_2c_conv_3[0][0]
block_2b_relu[0][0]
__________________________________________________________________________________________________
block_2c_relu (Activation) (None, 512, 28, 28) 0 add_6[0][0]
__________________________________________________________________________________________________
block_2d_conv_1 (Conv2D) (None, 128, 28, 28) 65536 block_2c_relu[0][0]
__________________________________________________________________________________________________
block_2d_relu_1 (Activation) (None, 128, 28, 28) 0 block_2d_conv_1[0][0]
__________________________________________________________________________________________________
block_2d_conv_2 (Conv2D) (None, 128, 28, 28) 147456 block_2d_relu_1[0][0]
__________________________________________________________________________________________________
block_2d_relu_2 (Activation) (None, 128, 28, 28) 0 block_2d_conv_2[0][0]
__________________________________________________________________________________________________
block_2d_conv_3 (Conv2D) (None, 512, 28, 28) 65536 block_2d_relu_2[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 512, 28, 28) 0 block_2d_conv_3[0][0]
block_2c_relu[0][0]
__________________________________________________________________________________________________
block_2d_relu (Activation) (None, 512, 28, 28) 0 add_7[0][0]
__________________________________________________________________________________________________
block_3a_conv_1 (Conv2D) (None, 256, 14, 14) 131072 block_2d_relu[0][0]
__________________________________________________________________________________________________
block_3a_relu_1 (Activation) (None, 256, 14, 14) 0 block_3a_conv_1[0][0]
__________________________________________________________________________________________________
block_3a_conv_2 (Conv2D) (None, 256, 14, 14) 589824 block_3a_relu_1[0][0]
__________________________________________________________________________________________________
block_3a_relu_2 (Activation) (None, 256, 14, 14) 0 block_3a_conv_2[0][0]
__________________________________________________________________________________________________
block_3a_conv_3 (Conv2D) (None, 1024, 14, 14) 262144 block_3a_relu_2[0][0]
__________________________________________________________________________________________________
block_3a_conv_shortcut (Conv2D) (None, 1024, 14, 14) 524288 block_2d_relu[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 1024, 14, 14) 0 block_3a_conv_3[0][0]
block_3a_conv_shortcut[0][0]
__________________________________________________________________________________________________
block_3a_relu (Activation) (None, 1024, 14, 14) 0 add_8[0][0]
__________________________________________________________________________________________________
block_3b_conv_1 (Conv2D) (None, 256, 14, 14) 262144 block_3a_relu[0][0]
__________________________________________________________________________________________________
block_3b_relu_1 (Activation) (None, 256, 14, 14) 0 block_3b_conv_1[0][0]
__________________________________________________________________________________________________
block_3b_conv_2 (Conv2D) (None, 256, 14, 14) 589824 block_3b_relu_1[0][0]
__________________________________________________________________________________________________
block_3b_relu_2 (Activation) (None, 256, 14, 14) 0 block_3b_conv_2[0][0]
__________________________________________________________________________________________________
block_3b_conv_3 (Conv2D) (None, 1024, 14, 14) 262144 block_3b_relu_2[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 1024, 14, 14) 0 block_3b_conv_3[0][0]
block_3a_relu[0][0]
__________________________________________________________________________________________________
block_3b_relu (Activation) (None, 1024, 14, 14) 0 add_9[0][0]
__________________________________________________________________________________________________
block_3c_conv_1 (Conv2D) (None, 256, 14, 14) 262144 block_3b_relu[0][0]
__________________________________________________________________________________________________
block_3c_relu_1 (Activation) (None, 256, 14, 14) 0 block_3c_conv_1[0][0]
__________________________________________________________________________________________________
block_3c_conv_2 (Conv2D) (None, 256, 14, 14) 589824 block_3c_relu_1[0][0]
__________________________________________________________________________________________________
block_3c_relu_2 (Activation) (None, 256, 14, 14) 0 block_3c_conv_2[0][0]
__________________________________________________________________________________________________
block_3c_conv_3 (Conv2D) (None, 1024, 14, 14) 262144 block_3c_relu_2[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 1024, 14, 14) 0 block_3c_conv_3[0][0]
block_3b_relu[0][0]
__________________________________________________________________________________________________
block_3c_relu (Activation) (None, 1024, 14, 14) 0 add_10[0][0]
__________________________________________________________________________________________________
block_3d_conv_1 (Conv2D) (None, 256, 14, 14) 262144 block_3c_relu[0][0]
__________________________________________________________________________________________________
block_3d_relu_1 (Activation) (None, 256, 14, 14) 0 block_3d_conv_1[0][0]
__________________________________________________________________________________________________
block_3d_conv_2 (Conv2D) (None, 256, 14, 14) 589824 block_3d_relu_1[0][0]
__________________________________________________________________________________________________
block_3d_relu_2 (Activation) (None, 256, 14, 14) 0 block_3d_conv_2[0][0]
__________________________________________________________________________________________________
block_3d_conv_3 (Conv2D) (None, 1024, 14, 14) 262144 block_3d_relu_2[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, 1024, 14, 14) 0 block_3d_conv_3[0][0]
block_3c_relu[0][0]
__________________________________________________________________________________________________
block_3d_relu (Activation) (None, 1024, 14, 14) 0 add_11[0][0]
__________________________________________________________________________________________________
block_3e_conv_1 (Conv2D) (None, 256, 14, 14) 262144 block_3d_relu[0][0]
__________________________________________________________________________________________________
block_3e_relu_1 (Activation) (None, 256, 14, 14) 0 block_3e_conv_1[0][0]
__________________________________________________________________________________________________
block_3e_conv_2 (Conv2D) (None, 256, 14, 14) 589824 block_3e_relu_1[0][0]
__________________________________________________________________________________________________
block_3e_relu_2 (Activation) (None, 256, 14, 14) 0 block_3e_conv_2[0][0]
__________________________________________________________________________________________________
block_3e_conv_3 (Conv2D) (None, 1024, 14, 14) 262144 block_3e_relu_2[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, 1024, 14, 14) 0 block_3e_conv_3[0][0]
block_3d_relu[0][0]
__________________________________________________________________________________________________
block_3e_relu (Activation) (None, 1024, 14, 14) 0 add_12[0][0]
__________________________________________________________________________________________________
block_3f_conv_1 (Conv2D) (None, 256, 14, 14) 262144 block_3e_relu[0][0]
__________________________________________________________________________________________________
block_3f_relu_1 (Activation) (None, 256, 14, 14) 0 block_3f_conv_1[0][0]
__________________________________________________________________________________________________
block_3f_conv_2 (Conv2D) (None, 256, 14, 14) 589824 block_3f_relu_1[0][0]
__________________________________________________________________________________________________
block_3f_relu_2 (Activation) (None, 256, 14, 14) 0 block_3f_conv_2[0][0]
__________________________________________________________________________________________________
block_3f_conv_3 (Conv2D) (None, 1024, 14, 14) 262144 block_3f_relu_2[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, 1024, 14, 14) 0 block_3f_conv_3[0][0]
block_3e_relu[0][0]
__________________________________________________________________________________________________
block_3f_relu (Activation) (None, 1024, 14, 14) 0 add_13[0][0]
__________________________________________________________________________________________________
block_4a_conv_1 (Conv2D) (None, 512, 14, 14) 524288 block_3f_relu[0][0]
__________________________________________________________________________________________________
block_4a_relu_1 (Activation) (None, 512, 14, 14) 0 block_4a_conv_1[0][0]
__________________________________________________________________________________________________
block_4a_conv_2 (Conv2D) (None, 512, 14, 14) 2359296 block_4a_relu_1[0][0]
__________________________________________________________________________________________________
block_4a_relu_2 (Activation) (None, 512, 14, 14) 0 block_4a_conv_2[0][0]
__________________________________________________________________________________________________
block_4a_conv_3 (Conv2D) (None, 2048, 14, 14) 1048576 block_4a_relu_2[0][0]
__________________________________________________________________________________________________
block_4a_conv_shortcut (Conv2D) (None, 2048, 14, 14) 2097152 block_3f_relu[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, 2048, 14, 14) 0 block_4a_conv_3[0][0]
block_4a_conv_shortcut[0][0]
__________________________________________________________________________________________________
block_4a_relu (Activation) (None, 2048, 14, 14) 0 add_14[0][0]
__________________________________________________________________________________________________
block_4b_conv_1 (Conv2D) (None, 512, 14, 14) 1048576 block_4a_relu[0][0]
__________________________________________________________________________________________________
block_4b_relu_1 (Activation) (None, 512, 14, 14) 0 block_4b_conv_1[0][0]
__________________________________________________________________________________________________
block_4b_conv_2 (Conv2D) (None, 512, 14, 14) 2359296 block_4b_relu_1[0][0]
__________________________________________________________________________________________________
block_4b_relu_2 (Activation) (None, 512, 14, 14) 0 block_4b_conv_2[0][0]
__________________________________________________________________________________________________
block_4b_conv_3 (Conv2D) (None, 2048, 14, 14) 1048576 block_4b_relu_2[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, 2048, 14, 14) 0 block_4b_conv_3[0][0]
block_4a_relu[0][0]
__________________________________________________________________________________________________
block_4b_relu (Activation) (None, 2048, 14, 14) 0 add_15[0][0]
__________________________________________________________________________________________________
block_4c_conv_1 (Conv2D) (None, 512, 14, 14) 1048576 block_4b_relu[0][0]
__________________________________________________________________________________________________
block_4c_relu_1 (Activation) (None, 512, 14, 14) 0 block_4c_conv_1[0][0]
__________________________________________________________________________________________________
INFO: Processing dataset (evaluation): /workspace/tao-experiments/data/val
INFO: Calculating per-class P/R and confusion matrix. It may take a while...
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
block_4c_conv_2 (Conv2D) (None, 512, 14, 14) 2359296 block_4c_relu_1[0][0]
__________________________________________________________________________________________________
block_4c_relu_2 (Activation) (None, 512, 14, 14) 0 block_4c_conv_2[0][0]
__________________________________________________________________________________________________
block_4c_conv_3 (Conv2D) (None, 2048, 14, 14) 1048576 block_4c_relu_2[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, 2048, 14, 14) 0 block_4c_conv_3[0][0]
block_4b_relu[0][0]
__________________________________________________________________________________________________
block_4c_relu (Activation) (None, 2048, 14, 14) 0 add_16[0][0]
__________________________________________________________________________________________________
avg_pool (AveragePooling2D) (None, 2048, 1, 1) 0 block_4c_relu[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 2048) 0 avg_pool[0][0]
__________________________________________________________________________________________________
predictions (Dense) (None, 4) 8196 flatten[0][0]
==================================================================================================
Total params: 23,463,108
Trainable params: 23,453,700
Non-trainable params: 9,408
__________________________________________________________________________________________________
Found 36 images belonging to 4 classes.
Evaluation Loss: 2.877385245429145
Evaluation Top K accuracy: 0.8333333333333334
Found 36 images belonging to 4 classes.
Confusion Matrix
[[30 0 0]
[ 4 0 0]
[ 2 0 0]]
Classification Report
precision recall f1-score support
C 0.83 1.00 0.91 30
Q 0.00 0.00 0.00 0
R 0.00 0.00 0.00 4
U 0.00 0.00 0.00 2
micro avg 0.83 0.83 0.83 36
macro avg 0.21 0.25 0.23 36
weighted avg 0.69 0.83 0.76 36
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