| {"format": "graph-model", "modelTopology": {"node": [{"name": "x", "op": "Placeholder", "attr": {"shape": {"shape": {"dim": [{"size": "-1"}, {"size": "-1"}, {"size": "-1"}, {"size": "16"}]}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "step_size", "op": "Placeholder", "attr": {"dtype": {"type": "DT_FLOAT"}, "shape": {"shape": {}}}}, {"name": "angle", "op": "Placeholder", "attr": {"shape": {"shape": {}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "fire_rate", "op": "Placeholder", "attr": {"shape": {"shape": {}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "strided_slice/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAwAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "strided_slice/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAABAAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "strided_slice/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AQAAAAEAAAABAAAAAQAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "strided_slice", "op": "StridedSlice", "input": ["x", "strided_slice/stack", "strided_slice/stack_1", "strided_slice/stack_2"], "attr": {"new_axis_mask": {"i": "0"}, "Index": {"type": "DT_INT32"}, "ellipsis_mask": {"i": "0"}, "end_mask": {"i": "7"}, "begin_mask": {"i": "7"}, "shrink_axis_mask": {"i": "0"}, "T": {"type": "DT_FLOAT"}}}, {"name": "MaxPool2d", "op": "MaxPool", "input": ["strided_slice"], "attr": {"padding": {"s": "U0FNRQ=="}, "explicit_paddings": {"list": {}}, "data_format": {"s": "TkhXQw=="}, "T": {"type": "DT_FLOAT"}, "strides": {"list": {"i": ["1", "1", "1", "1"]}}, "ksize": {"list": {"i": ["1", "3", "3", "1"]}}}}, {"name": "Greater/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}, "floatVal": [0.1]}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "Greater", "op": "Greater", "input": ["MaxPool2d", "Greater/y"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "Func/PartitionedCall/input/_0", "op": "Identity", "input": ["x"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/stack_1/values_0", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAAAAAAAAgD8AAAAAAAAAAAAAAAAAAAAA"}}}}, {"name": "Func/PartitionedCall/input/_1", "op": "Identity", "input": ["angle"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/Cos", "op": "Cos", "input": ["Func/PartitionedCall/input/_1"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}]}, "tensorContent": "AAAAvgAAAAAAAAA+AACAvgAAAAAAAIA+AAAAvgAAAAAAAAA+"}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul", "op": "Mul", "input": ["PartitionedCall/Cos", "PartitionedCall/mul/y"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/Sin", "op": "Sin", "input": ["Func/PartitionedCall/input/_1"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul_1/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}]}, "tensorContent": "AAAAvgAAgL4AAAC+AAAAAAAAAAAAAAAAAAAAPgAAgD4AAAA+"}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul_1", "op": "Mul", "input": ["PartitionedCall/Sin", "PartitionedCall/mul_1/y"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/sub", "op": "Sub", "input": ["PartitionedCall/mul", "PartitionedCall/mul_1"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul_2/y", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}]}, "tensorContent": "AAAAvgAAAAAAAAA+AACAvgAAAAAAAIA+AAAAvgAAAAAAAAA+"}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul_2", "op": "Mul", "input": ["PartitionedCall/Sin", "PartitionedCall/mul_2/y"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/mul_3/y", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}]}, "tensorContent": "AAAAvgAAgL4AAAC+AAAAAAAAAAAAAAAAAAAAPgAAgD4AAAA+"}}}}, {"name": "PartitionedCall/mul_3", "op": "Mul", "input": ["PartitionedCall/Cos", "PartitionedCall/mul_3/y"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/add", "op": "AddV2", "input": ["PartitionedCall/mul_2", "PartitionedCall/mul_3"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/stack_1", "op": "Pack", "input": ["PartitionedCall/stack_1/values_0", "PartitionedCall/sub", "PartitionedCall/add"], "attr": {"N": {"i": "3"}, "T": {"type": "DT_FLOAT"}, "axis": {"i": "-1"}}}, {"name": "PartitionedCall/strided_slice/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAAAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/strided_slice/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAAAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/strided_slice/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AQAAAAEAAAABAAAAAQAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/strided_slice", "op": "StridedSlice", "input": ["PartitionedCall/stack_1", "PartitionedCall/strided_slice/stack", "PartitionedCall/strided_slice/stack_1", "PartitionedCall/strided_slice/stack_2"], "attr": {"ellipsis_mask": {"i": "0"}, "shrink_axis_mask": {"i": "0"}, "begin_mask": {"i": "11"}, "Index": {"type": "DT_INT32"}, "end_mask": {"i": "11"}, "T": {"type": "DT_FLOAT"}, "new_axis_mask": {"i": "4"}}}, {"name": "PartitionedCall/Repeat/ExpandDims/dim", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [3]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/ExpandDims", "op": "ExpandDims", "input": ["PartitionedCall/strided_slice", "PartitionedCall/Repeat/ExpandDims/dim"], "attr": {"Tdim": {"type": "DT_INT32"}, "T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/Repeat/Tile/multiples/0", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [1]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/Tile/multiples/1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [1]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/Tile/multiples/2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [1]}}}}, {"name": "PartitionedCall/Repeat/repeats", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [16]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/Reshape/shape_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{}]}}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/Reshape", "op": "Reshape", "input": ["PartitionedCall/Repeat/repeats", "PartitionedCall/Repeat/Reshape/shape_1"], "attr": {"T": {"type": "DT_INT32"}, "Tshape": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/Tile/multiples/4", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [1]}}}}, {"name": "PartitionedCall/Repeat/Tile/multiples", "op": "Pack", "input": ["PartitionedCall/Repeat/Tile/multiples/0", "PartitionedCall/Repeat/Tile/multiples/1", "PartitionedCall/Repeat/Tile/multiples/2", "PartitionedCall/Repeat/Reshape", "PartitionedCall/Repeat/Tile/multiples/4"], "attr": {"T": {"type": "DT_INT32"}, "axis": {"i": "0"}, "N": {"i": "5"}}}, {"name": "PartitionedCall/Repeat/Tile", "op": "Tile", "input": ["PartitionedCall/Repeat/ExpandDims", "PartitionedCall/Repeat/Tile/multiples"], "attr": {"Tmultiples": {"type": "DT_INT32"}, "T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/Repeat/Shape", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AwAAAAMAAAABAAAAAwAAAA=="}}}}, {"name": "PartitionedCall/Repeat/strided_slice/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [0]}}}}, {"name": "PartitionedCall/Repeat/strided_slice/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [2]}}}}, {"name": "PartitionedCall/Repeat/strided_slice/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [1]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/strided_slice", "op": "StridedSlice", "input": ["PartitionedCall/Repeat/Shape", "PartitionedCall/Repeat/strided_slice/stack", "PartitionedCall/Repeat/strided_slice/stack_1", "PartitionedCall/Repeat/strided_slice/stack_2"], "attr": {"Index": {"type": "DT_INT32"}, "begin_mask": {"i": "1"}, "new_axis_mask": {"i": "0"}, "shrink_axis_mask": {"i": "0"}, "end_mask": {"i": "0"}, "T": {"type": "DT_INT32"}, "ellipsis_mask": {"i": "0"}}}, {"name": "PartitionedCall/Repeat/strided_slice_1/stack", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [2]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/strided_slice_1/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [3]}}}}, {"name": "PartitionedCall/Repeat/strided_slice_1/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [1]}}}}, {"name": "PartitionedCall/Repeat/strided_slice_1", "op": "StridedSlice", "input": ["PartitionedCall/Repeat/Shape", "PartitionedCall/Repeat/strided_slice_1/stack", "PartitionedCall/Repeat/strided_slice_1/stack_1", "PartitionedCall/Repeat/strided_slice_1/stack_2"], "attr": {"end_mask": {"i": "0"}, "ellipsis_mask": {"i": "0"}, "new_axis_mask": {"i": "0"}, "begin_mask": {"i": "0"}, "T": {"type": "DT_INT32"}, "shrink_axis_mask": {"i": "1"}, "Index": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/mul", "op": "Mul", "input": ["PartitionedCall/Repeat/Reshape", "PartitionedCall/Repeat/strided_slice_1"], "attr": {"T": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/concat/values_1", "op": "Pack", "input": ["PartitionedCall/Repeat/mul"], "attr": {"N": {"i": "1"}, "axis": {"i": "0"}, "T": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/strided_slice_2/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [3]}}}}, {"name": "PartitionedCall/Repeat/strided_slice_2/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [0]}}}}, {"name": "PartitionedCall/Repeat/strided_slice_2/stack_2", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "1"}]}, "intVal": [1]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/strided_slice_2", "op": "StridedSlice", "input": ["PartitionedCall/Repeat/Shape", "PartitionedCall/Repeat/strided_slice_2/stack", "PartitionedCall/Repeat/strided_slice_2/stack_1", "PartitionedCall/Repeat/strided_slice_2/stack_2"], "attr": {"T": {"type": "DT_INT32"}, "shrink_axis_mask": {"i": "0"}, "begin_mask": {"i": "0"}, "Index": {"type": "DT_INT32"}, "new_axis_mask": {"i": "0"}, "ellipsis_mask": {"i": "0"}, "end_mask": {"i": "1"}}}, {"name": "PartitionedCall/Repeat/concat/axis", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {}, "intVal": [0]}}, "dtype": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/concat", "op": "ConcatV2", "input": ["PartitionedCall/Repeat/strided_slice", "PartitionedCall/Repeat/concat/values_1", "PartitionedCall/Repeat/strided_slice_2", "PartitionedCall/Repeat/concat/axis"], "attr": {"T": {"type": "DT_INT32"}, "N": {"i": "3"}, "Tidx": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/Repeat/Reshape_1", "op": "Reshape", "input": ["PartitionedCall/Repeat/Tile", "PartitionedCall/Repeat/concat"], "attr": {"T": {"type": "DT_FLOAT"}, "Tshape": {"type": "DT_INT32"}}}, {"name": "PartitionedCall/depthwise", "op": "DepthwiseConv2dNative", "input": ["Func/PartitionedCall/input/_0", "PartitionedCall/Repeat/Reshape_1"], "attr": {"explicit_paddings": {"list": {}}, "dilations": {"list": {"i": ["1", "1", "1", "1"]}}, "strides": {"list": {"i": ["1", "1", "1", "1"]}}, "data_format": {"s": "TkhXQw=="}, "padding": {"s": "U0FNRQ=="}, "T": {"type": "DT_FLOAT"}}}, {"name": "PartitionedCall/Identity", "op": "Identity", "input": ["PartitionedCall/depthwise"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "Func/PartitionedCall/output/_2", "op": "Identity", "input": ["PartitionedCall/Identity"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_2/Conv2D/ReadVariableOp/resource", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "48"}, {"size": "128"}]}, "tensorContent": "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"}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_2/Conv2D/ReadVariableOp", "op": "Identity", "input": ["sequential_1/conv2d_2/Conv2D/ReadVariableOp/resource"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_2/Conv2D", "op": "Conv2D", "input": ["Func/PartitionedCall/output/_2", "sequential_1/conv2d_2/Conv2D/ReadVariableOp"], "attr": {"dilations": {"list": {"i": ["1", "1", "1", "1"]}}, "T": {"type": "DT_FLOAT"}, "strides": {"list": {"i": ["1", "1", "1", "1"]}}, "data_format": {"s": "TkhXQw=="}, "padding": {"s": "VkFMSUQ="}, "explicit_paddings": {"list": {}}, "use_cudnn_on_gpu": {"b": true}}}, {"name": "sequential_1/conv2d_2/BiasAdd/ReadVariableOp/resource", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "128"}]}, "tensorContent": "T8/uu8CSnT1QC36954QGPkmHn71piwO+ExsbPS/tET7YnZc919o5PEtASr6P8ei95j4PPWFhBTzlg0W9juaIvAJoxL2aKzI97PJdvNSeXjofvLm9Rtj8vKBjdb1XqO+9FCogPTBXg70Kqga9C3UXPoHWDL5Qims8TevxvCUc0j3PTzS9Sm+TvWQunL0d9Mw9nSYwvZw9wjuJfG49mvUovRr5ib3iRwu+LxwIPaSdJzx92dY9sTfmPAXz3r1eyAw9SofbPDUZt7sxs769hgqRPakTqLxlSEO93Jdeu3uSiztMoqu9m6PGvYRDaj0B3x88yueovbcSTD37x3g9St/AvWP/szv0qEQ9QK3hvVPqmjwpHZe6a6K1vUiEBr4ck+C9o2nLvLckD71Dp++9rAn0PA8M7r0sPuG7MG4JvEeCBTypR6G8LKwHPIuajr39Z7C97rYcvWGlZL2C7nK8Jz1QvDCKaTyuV7s85M6zvf4KU71/AFg8j9LburYVKb6nHn+96wGtvUP0JT2KSo+9B0v8PCJFEb3N9Ag8Cfw1Pc+ku70ppam9LOU0Ponp07xC1Xc9vfMHvmxxSb0SziW+/HGNPV3XhL303X88gU1Hvcy28LueGJO9dxjCvanSYD3T8M69teoKPN6E4zwoZqe8wHefO3wkZj2W73q9J7gUPcSIc7w="}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_2/BiasAdd/ReadVariableOp", "op": "Identity", "input": ["sequential_1/conv2d_2/BiasAdd/ReadVariableOp/resource"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_2/BiasAdd", "op": "BiasAdd", "input": ["sequential_1/conv2d_2/Conv2D", "sequential_1/conv2d_2/BiasAdd/ReadVariableOp"], "attr": {"T": {"type": "DT_FLOAT"}, "data_format": {"s": "TkhXQw=="}}}, {"name": "sequential_1/conv2d_2/Relu", "op": "Relu", "input": ["sequential_1/conv2d_2/BiasAdd"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_3/Conv2D/ReadVariableOp/resource", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "128"}, {"size": "16"}]}, "tensorContent": "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"}}}}, {"name": "sequential_1/conv2d_3/Conv2D/ReadVariableOp", "op": "Identity", "input": ["sequential_1/conv2d_3/Conv2D/ReadVariableOp/resource"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_3/Conv2D", "op": "Conv2D", "input": ["sequential_1/conv2d_2/Relu", "sequential_1/conv2d_3/Conv2D/ReadVariableOp"], "attr": {"strides": {"list": {"i": ["1", "1", "1", "1"]}}, "data_format": {"s": "TkhXQw=="}, "padding": {"s": "VkFMSUQ="}, "use_cudnn_on_gpu": {"b": true}, "T": {"type": "DT_FLOAT"}, "explicit_paddings": {"list": {}}, "dilations": {"list": {"i": ["1", "1", "1", "1"]}}}}, {"name": "sequential_1/conv2d_3/BiasAdd/ReadVariableOp/resource", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "16"}]}, "tensorContent": "k6eWPG+fHTzY9/U8RlwbPaDYgz3BiGm9RV0oPWS/Kz1cLhI9VCTFvAqwgjzcaUE9sX/MOyN/Cb2RWSy9IcqTvA=="}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_3/BiasAdd/ReadVariableOp", "op": "Identity", "input": ["sequential_1/conv2d_3/BiasAdd/ReadVariableOp/resource"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "sequential_1/conv2d_3/BiasAdd", "op": "BiasAdd", "input": ["sequential_1/conv2d_3/Conv2D", "sequential_1/conv2d_3/BiasAdd/ReadVariableOp"], "attr": {"data_format": {"s": "TkhXQw=="}, "T": {"type": "DT_FLOAT"}}}, {"name": "mul", "op": "Mul", "input": ["sequential_1/conv2d_3/BiasAdd", "step_size"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "strided_slice_1/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAAAAAA=="}}}}, {"name": "strided_slice_1/stack_1", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAQAAAA=="}}}}, {"name": "strided_slice_1/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AQAAAAEAAAABAAAAAQAAAA=="}}}}, {"name": "strided_slice_1", "op": "StridedSlice", "input": ["x", "strided_slice_1/stack", "strided_slice_1/stack_1", "strided_slice_1/stack_2"], "attr": {"ellipsis_mask": {"i": "0"}, "Index": {"type": "DT_INT32"}, "new_axis_mask": {"i": "0"}, "end_mask": {"i": "7"}, "shrink_axis_mask": {"i": "0"}, "T": {"type": "DT_FLOAT"}, "begin_mask": {"i": "15"}}}, {"name": "Shape", "op": "Shape", "input": ["strided_slice_1"], "attr": {"T": {"type": "DT_FLOAT"}, "out_type": {"type": "DT_INT32"}}}, {"name": "random_uniform/RandomUniform", "op": "RandomUniform", "input": ["Shape"], "attr": {"T": {"type": "DT_INT32"}, "seed": {"i": "0"}, "seed2": {"i": "0"}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "LessEqual", "op": "LessEqual", "input": ["random_uniform/RandomUniform", "fire_rate"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "Cast", "op": "Cast", "input": ["LessEqual"], "attr": {"Truncate": {"b": false}, "SrcT": {"type": "DT_BOOL"}, "DstT": {"type": "DT_FLOAT"}}}, {"name": "mul_1", "op": "Mul", "input": ["mul", "Cast"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "add", "op": "AddV2", "input": ["x", "mul_1"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "strided_slice_2/stack", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAAAwAAAA=="}}}}, {"name": "strided_slice_2/stack_1", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AAAAAAAAAAAAAAAABAAAAA=="}}, "dtype": {"type": "DT_INT32"}}}, {"name": "strided_slice_2/stack_2", "op": "Const", "attr": {"dtype": {"type": "DT_INT32"}, "value": {"tensor": {"dtype": "DT_INT32", "tensorShape": {"dim": [{"size": "4"}]}, "tensorContent": "AQAAAAEAAAABAAAAAQAAAA=="}}}}, {"name": "strided_slice_2", "op": "StridedSlice", "input": ["add", "strided_slice_2/stack", "strided_slice_2/stack_1", "strided_slice_2/stack_2"], "attr": {"shrink_axis_mask": {"i": "0"}, "begin_mask": {"i": "7"}, "T": {"type": "DT_FLOAT"}, "ellipsis_mask": {"i": "0"}, "end_mask": {"i": "7"}, "Index": {"type": "DT_INT32"}, "new_axis_mask": {"i": "0"}}}, {"name": "MaxPool2d_1", "op": "MaxPool", "input": ["strided_slice_2"], "attr": {"T": {"type": "DT_FLOAT"}, "explicit_paddings": {"list": {}}, "padding": {"s": "U0FNRQ=="}, "ksize": {"list": {"i": ["1", "3", "3", "1"]}}, "strides": {"list": {"i": ["1", "1", "1", "1"]}}, "data_format": {"s": "TkhXQw=="}}}, {"name": "Greater_1/y", "op": "Const", "attr": {"dtype": {"type": "DT_FLOAT"}, "value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {}, "floatVal": [0.1]}}}}, {"name": "Greater_1", "op": "Greater", "input": ["MaxPool2d_1", "Greater_1/y"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "and", "op": "LogicalAnd", "input": ["Greater", "Greater_1"]}, {"name": "Cast_1", "op": "Cast", "input": ["and"], "attr": {"DstT": {"type": "DT_FLOAT"}, "Truncate": {"b": false}, "SrcT": {"type": "DT_BOOL"}}}, {"name": "mul_2", "op": "Mul", "input": ["add", "Cast_1"], "attr": {"T": {"type": "DT_FLOAT"}}}, {"name": "NoOp", "op": "NoOp", "input": ["^sequential_1/conv2d_2/BiasAdd/ReadVariableOp", "^sequential_1/conv2d_2/Conv2D/ReadVariableOp", "^sequential_1/conv2d_3/BiasAdd/ReadVariableOp", "^sequential_1/conv2d_3/Conv2D/ReadVariableOp"], "attr": {"_acd_function_control_output": {"b": true}}}, {"name": "Identity", "op": "Identity", "input": ["mul_2", "^NoOp"], "attr": {"T": {"type": "DT_FLOAT"}}}], "versions": {"producer": "1.14", "minConsumer": "1.14"}}, "weightsManifest": []} |