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