From 7410c6bb02477d21df9398313ad2cd7e8d22090a Mon Sep 17 00:00:00 2001 From: Pi314 Date: Fri, 12 Jun 2020 16:28:41 -0700 Subject: [PATCH] Update LuNet.ipynb --- LuNet.ipynb | 330 ++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 294 insertions(+), 36 deletions(-) diff --git a/LuNet.ipynb b/LuNet.ipynb index 75fb3a1..3b85079 100644 --- a/LuNet.ipynb +++ b/LuNet.ipynb @@ -6,47 +6,302 @@ "source": [ "## LuNet\n", "\n", - "as described in : https://arxiv.org/pdf/1703.07737.pdf implemented in tensorflow 2 / keras" + "as described in : https://arxiv.org/pdf/1703.07737.pdf" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { - "ename": "InternalError", - "evalue": "CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInternalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0minput_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_layer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMaxPool2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'same'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mdefault_variable_creator\u001b[0;34m(next_creator, **kwargs)\u001b[0m\n\u001b[1;32m 2596\u001b[0m \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2597\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2598\u001b[0;31m shape=shape)\n\u001b[0m\u001b[1;32m 2599\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2600\u001b[0m return variables.RefVariable(\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_v2_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVariableMetaclass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/ops/init_ops_v2.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(self, shape, minval, maxval, dtype)\u001b[0m\n\u001b[1;32m 1066\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_uniform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1067\u001b[0m return op(\n\u001b[0;32m-> 1068\u001b[0;31m shape=shape, minval=minval, maxval=maxval, dtype=dtype, seed=self.seed)\n\u001b[0m\u001b[1;32m 1069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1070\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtruncated_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstddev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, minval, maxval, dtype, seed, name)\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0mmaxval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"random_uniform\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 283\u001b[0m \u001b[0;31m# In case of [0,1) floating results, minval and maxval is unused. We do an\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;31m# `is` comparison here since this is cheaper than isinstance or __eq__.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py\u001b[0m in \u001b[0;36mshape_tensor\u001b[0;34m(shape)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;31m# not convertible to Tensors because of mixed content.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimension_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"shape\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1017\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m 1339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1340\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1341\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1343\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_tensor_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m 319\u001b[0m as_ref=False):\n\u001b[1;32m 320\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m 260\u001b[0m \"\"\"\n\u001b[1;32m 261\u001b[0m return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 262\u001b[0;31m allow_broadcast=True)\n\u001b[0m\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 271\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 272\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/DeepLearning2/lib/python3.6/site-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36mensure_initialized\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 513\u001b[0m pywrap_tfe.TFE_ContextOptionsSetLazyRemoteInputsCopy(\n\u001b[1;32m 514\u001b[0m opts, self._lazy_remote_inputs_copy)\n\u001b[0;32m--> 515\u001b[0;31m \u001b[0mcontext_handle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpywrap_tfe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFE_NewContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 516\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0mpywrap_tfe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFE_DeleteContextOptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mInternalError\u001b[0m: CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory" + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_2\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_4 (InputLayer) [(None, 128, 64, 3)] 0 \n", + "__________________________________________________________________________________________________\n", + "conv2d_88 (Conv2D) (None, 122, 58, 128) 18944 input_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_89 (Conv2D) (None, 122, 58, 128) 16512 conv2d_88[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_72 (BatchNo (None, 122, 58, 128) 512 conv2d_89[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_70 (LeakyReLU) (None, 122, 58, 128) 0 batch_normalization_72[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_90 (Conv2D) (None, 122, 58, 32) 36896 leaky_re_lu_70[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_73 (BatchNo (None, 122, 58, 32) 128 conv2d_90[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_71 (LeakyReLU) (None, 122, 58, 32) 0 batch_normalization_73[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_91 (Conv2D) (None, 122, 58, 128) 4224 leaky_re_lu_71[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_74 (BatchNo (None, 122, 58, 128) 512 conv2d_91[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_72 (LeakyReLU) (None, 122, 58, 128) 0 batch_normalization_74[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_15 (Add) (None, 122, 58, 128) 0 conv2d_88[0][0] \n", + " leaky_re_lu_72[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_10 (MaxPooling2D) (None, 61, 29, 128) 0 add_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_92 (Conv2D) (None, 61, 29, 128) 16512 max_pooling2d_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_75 (BatchNo (None, 61, 29, 128) 512 conv2d_92[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_73 (LeakyReLU) (None, 61, 29, 128) 0 batch_normalization_75[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_93 (Conv2D) (None, 61, 29, 32) 36896 leaky_re_lu_73[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_76 (BatchNo (None, 61, 29, 32) 128 conv2d_93[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_74 (LeakyReLU) (None, 61, 29, 32) 0 batch_normalization_76[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_94 (Conv2D) (None, 61, 29, 128) 4224 leaky_re_lu_74[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_77 (BatchNo (None, 61, 29, 128) 512 conv2d_94[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_75 (LeakyReLU) (None, 61, 29, 128) 0 batch_normalization_77[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_16 (Add) (None, 61, 29, 128) 0 max_pooling2d_10[0][0] \n", + " leaky_re_lu_75[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_95 (Conv2D) (None, 61, 29, 128) 16512 add_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_78 (BatchNo (None, 61, 29, 128) 512 conv2d_95[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_76 (LeakyReLU) (None, 61, 29, 128) 0 batch_normalization_78[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_96 (Conv2D) (None, 61, 29, 32) 36896 leaky_re_lu_76[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_79 (BatchNo (None, 61, 29, 32) 128 conv2d_96[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_77 (LeakyReLU) (None, 61, 29, 32) 0 batch_normalization_79[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_97 (Conv2D) (None, 61, 29, 128) 4224 leaky_re_lu_77[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_80 (BatchNo (None, 61, 29, 128) 512 conv2d_97[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_78 (LeakyReLU) (None, 61, 29, 128) 0 batch_normalization_80[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_17 (Add) (None, 61, 29, 128) 0 add_16[0][0] \n", + " leaky_re_lu_78[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_98 (Conv2D) (None, 61, 29, 128) 16512 add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_81 (BatchNo (None, 61, 29, 128) 512 conv2d_98[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_79 (LeakyReLU) (None, 61, 29, 128) 0 batch_normalization_81[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_99 (Conv2D) (None, 61, 29, 64) 73792 leaky_re_lu_79[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_82 (BatchNo (None, 61, 29, 64) 256 conv2d_99[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_80 (LeakyReLU) (None, 61, 29, 64) 0 batch_normalization_82[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_100 (Conv2D) (None, 61, 29, 256) 16640 leaky_re_lu_80[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_83 (BatchNo (None, 61, 29, 256) 1024 conv2d_100[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_101 (Conv2D) (None, 61, 29, 256) 33024 add_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_81 (LeakyReLU) (None, 61, 29, 256) 0 batch_normalization_83[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_18 (Add) (None, 61, 29, 256) 0 conv2d_101[0][0] \n", + " leaky_re_lu_81[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_11 (MaxPooling2D) (None, 31, 15, 256) 0 add_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_102 (Conv2D) (None, 31, 15, 256) 65792 max_pooling2d_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_84 (BatchNo (None, 31, 15, 256) 1024 conv2d_102[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_82 (LeakyReLU) (None, 31, 15, 256) 0 batch_normalization_84[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_103 (Conv2D) (None, 31, 15, 64) 147520 leaky_re_lu_82[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_85 (BatchNo (None, 31, 15, 64) 256 conv2d_103[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_83 (LeakyReLU) (None, 31, 15, 64) 0 batch_normalization_85[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_104 (Conv2D) (None, 31, 15, 256) 16640 leaky_re_lu_83[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_86 (BatchNo (None, 31, 15, 256) 1024 conv2d_104[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_84 (LeakyReLU) (None, 31, 15, 256) 0 batch_normalization_86[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_19 (Add) (None, 31, 15, 256) 0 max_pooling2d_11[0][0] \n", + " leaky_re_lu_84[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_105 (Conv2D) (None, 31, 15, 256) 65792 add_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_87 (BatchNo (None, 31, 15, 256) 1024 conv2d_105[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_85 (LeakyReLU) (None, 31, 15, 256) 0 batch_normalization_87[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_106 (Conv2D) (None, 31, 15, 64) 147520 leaky_re_lu_85[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_88 (BatchNo (None, 31, 15, 64) 256 conv2d_106[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_86 (LeakyReLU) (None, 31, 15, 64) 0 batch_normalization_88[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_107 (Conv2D) (None, 31, 15, 256) 16640 leaky_re_lu_86[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_89 (BatchNo (None, 31, 15, 256) 1024 conv2d_107[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_87 (LeakyReLU) (None, 31, 15, 256) 0 batch_normalization_89[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_20 (Add) (None, 31, 15, 256) 0 add_19[0][0] \n", + " leaky_re_lu_87[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_12 (MaxPooling2D) (None, 16, 8, 256) 0 add_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_108 (Conv2D) (None, 16, 8, 256) 65792 max_pooling2d_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_90 (BatchNo (None, 16, 8, 256) 1024 conv2d_108[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_88 (LeakyReLU) (None, 16, 8, 256) 0 batch_normalization_90[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_109 (Conv2D) (None, 16, 8, 64) 147520 leaky_re_lu_88[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_91 (BatchNo (None, 16, 8, 64) 256 conv2d_109[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_89 (LeakyReLU) (None, 16, 8, 64) 0 batch_normalization_91[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_110 (Conv2D) (None, 16, 8, 256) 16640 leaky_re_lu_89[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_92 (BatchNo (None, 16, 8, 256) 1024 conv2d_110[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_90 (LeakyReLU) (None, 16, 8, 256) 0 batch_normalization_92[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_21 (Add) (None, 16, 8, 256) 0 max_pooling2d_12[0][0] \n", + " leaky_re_lu_90[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_111 (Conv2D) (None, 16, 8, 256) 65792 add_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_93 (BatchNo (None, 16, 8, 256) 1024 conv2d_111[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_91 (LeakyReLU) (None, 16, 8, 256) 0 batch_normalization_93[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_112 (Conv2D) (None, 16, 8, 64) 147520 leaky_re_lu_91[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_94 (BatchNo (None, 16, 8, 64) 256 conv2d_112[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_92 (LeakyReLU) (None, 16, 8, 64) 0 batch_normalization_94[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_113 (Conv2D) (None, 16, 8, 256) 16640 leaky_re_lu_92[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_95 (BatchNo (None, 16, 8, 256) 1024 conv2d_113[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_93 (LeakyReLU) (None, 16, 8, 256) 0 batch_normalization_95[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_22 (Add) (None, 16, 8, 256) 0 add_21[0][0] \n", + " leaky_re_lu_93[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_114 (Conv2D) (None, 16, 8, 256) 65792 add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_96 (BatchNo (None, 16, 8, 256) 1024 conv2d_114[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_94 (LeakyReLU) (None, 16, 8, 256) 0 batch_normalization_96[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_115 (Conv2D) (None, 16, 8, 128) 295040 leaky_re_lu_94[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_97 (BatchNo (None, 16, 8, 128) 512 conv2d_115[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_95 (LeakyReLU) (None, 16, 8, 128) 0 batch_normalization_97[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_116 (Conv2D) (None, 16, 8, 512) 66048 leaky_re_lu_95[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_98 (BatchNo (None, 16, 8, 512) 2048 conv2d_116[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_117 (Conv2D) (None, 16, 8, 512) 131584 add_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_96 (LeakyReLU) (None, 16, 8, 512) 0 batch_normalization_98[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_23 (Add) (None, 16, 8, 512) 0 conv2d_117[0][0] \n", + " leaky_re_lu_96[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_13 (MaxPooling2D) (None, 8, 4, 512) 0 add_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_118 (Conv2D) (None, 8, 4, 512) 262656 max_pooling2d_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_99 (BatchNo (None, 8, 4, 512) 2048 conv2d_118[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_97 (LeakyReLU) (None, 8, 4, 512) 0 batch_normalization_99[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_119 (Conv2D) (None, 8, 4, 128) 589952 leaky_re_lu_97[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_100 (BatchN (None, 8, 4, 128) 512 conv2d_119[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_98 (LeakyReLU) (None, 8, 4, 128) 0 batch_normalization_100[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_120 (Conv2D) (None, 8, 4, 512) 66048 leaky_re_lu_98[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_101 (BatchN (None, 8, 4, 512) 2048 conv2d_120[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_99 (LeakyReLU) (None, 8, 4, 512) 0 batch_normalization_101[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_24 (Add) (None, 8, 4, 512) 0 max_pooling2d_13[0][0] \n", + " leaky_re_lu_99[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_121 (Conv2D) (None, 8, 4, 512) 262656 add_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_102 (BatchN (None, 8, 4, 512) 2048 conv2d_121[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_100 (LeakyReLU) (None, 8, 4, 512) 0 batch_normalization_102[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_122 (Conv2D) (None, 8, 4, 128) 589952 leaky_re_lu_100[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_103 (BatchN (None, 8, 4, 128) 512 conv2d_122[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_101 (LeakyReLU) (None, 8, 4, 128) 0 batch_normalization_103[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_123 (Conv2D) (None, 8, 4, 512) 66048 leaky_re_lu_101[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_104 (BatchN (None, 8, 4, 512) 2048 conv2d_123[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_102 (LeakyReLU) (None, 8, 4, 512) 0 batch_normalization_104[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_25 (Add) (None, 8, 4, 512) 0 add_24[0][0] \n", + " leaky_re_lu_102[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling2d_14 (MaxPooling2D) (None, 4, 2, 512) 0 add_25[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_124 (Conv2D) (None, 4, 2, 512) 2359808 max_pooling2d_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_105 (BatchN (None, 4, 2, 512) 2048 conv2d_124[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_103 (LeakyReLU) (None, 4, 2, 512) 0 batch_normalization_105[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_125 (Conv2D) (None, 4, 2, 128) 589952 leaky_re_lu_103[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_106 (BatchN (None, 4, 2, 128) 512 conv2d_125[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_126 (Conv2D) (None, 4, 2, 128) 65664 max_pooling2d_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "leaky_re_lu_104 (LeakyReLU) (None, 4, 2, 128) 0 batch_normalization_106[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_26 (Add) (None, 4, 2, 128) 0 conv2d_126[0][0] \n", + " leaky_re_lu_104[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten_2 (Flatten) (None, 1024) 0 add_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_4 (Dense) (None, 512) 524800 flatten_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "batch_normalization_107 (BatchN (None, 512) 2048 dense_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "re_lu_2 (ReLU) (None, 512) 0 batch_normalization_107[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_5 (Dense) (None, 128) 65664 re_lu_2[0][0] \n", + "==================================================================================================\n", + "Total params: 7,285,152\n", + "Trainable params: 7,269,216\n", + "Non-trainable params: 15,936\n", + "__________________________________________________________________________________________________\n" ] } ], @@ -65,8 +320,11 @@ " x = Conv2D(filters=n3, kernel_size=(1,1), strides=1, padding='same', kernel_initializer=\"he_normal\")(x)\n", " x = BatchNormalization()(x)\n", " x = LeakyReLU(alpha=0.3)(x)\n", - " shortcut = Conv2D(filters=n3, kernel_size=(1,1), strides=1, padding='same', kernel_initializer='he_normal')(tensor)\n", - " x = Add()([shortcut, x])\n", + " if n3!=n1:\n", + " shortcut = Conv2D(filters=n3, kernel_size=(1,1), strides=1, padding='same', kernel_initializer='he_normal')(tensor)\n", + " x = Add()([shortcut, x])\n", + " else:\n", + " x = Add()([tensor, x])\n", " return x\n", " \n", "def ResBlock2(tensor, n1, n2):\n",