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August 27, 2021 14:01
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"name": "cvnn-fan_in-after-zrelu.ipynb", | |
"provenance": [], | |
"collapsed_sections": [] | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
}, | |
"language_info": { | |
"name": "python" | |
} | |
}, | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "HKUi_4Prw1U7" | |
}, | |
"source": [ | |
"!pip --quiet install tensorflow==2.5.0 cvnn==1.1.63" | |
], | |
"execution_count": 1, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "Dtxql95xwvcj" | |
}, | |
"source": [ | |
"import tensorflow as tf\n", | |
"import cvnn\n", | |
"import cvnn.layers\n", | |
"import cvnn.activations" | |
], | |
"execution_count": 2, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 35 | |
}, | |
"id": "KvYU_y4uwyOB", | |
"outputId": "56983158-79e2-43c8-8b4a-9ca22a37ea14" | |
}, | |
"source": [ | |
"tf.__version__" | |
], | |
"execution_count": 3, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"application/vnd.google.colaboratory.intrinsic+json": { | |
"type": "string" | |
}, | |
"text/plain": [ | |
"'2.5.0'" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 3 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 35 | |
}, | |
"id": "nH-JHxL3wzTz", | |
"outputId": "257f8899-f5ec-43db-c403-61602622133c" | |
}, | |
"source": [ | |
"cvnn.__version__" | |
], | |
"execution_count": 4, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"application/vnd.google.colaboratory.intrinsic+json": { | |
"type": "string" | |
}, | |
"text/plain": [ | |
"'1.1.63'" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 4 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 1000 | |
}, | |
"id": "n1eyzxUqxCPT", | |
"outputId": "f8a5dcbf-af86-4cce-cc45-dd8db36bd597" | |
}, | |
"source": [ | |
"for activation in cvnn.activations.act_dispatcher.keys():\n", | |
" print(activation)\n", | |
" model = tf.keras.Sequential([\n", | |
" cvnn.layers.ComplexInput((4)),\n", | |
" cvnn.layers.ComplexDense(1, activation=activation),\n", | |
" cvnn.layers.ComplexDense(1, activation='linear')\n", | |
" ])" | |
], | |
"execution_count": 5, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"linear\n", | |
"cast_to_real\n", | |
"convert_to_real_with_abs\n", | |
"sigmoid_real\n", | |
"softmax_real_with_abs\n", | |
"softmax_real_with_avg\n", | |
"softmax_real_with_mult\n", | |
"softmax_of_softmax_real_with_mult\n", | |
"softmax_of_softmax_real_with_avg\n", | |
"softmax_real_with_polar\n", | |
"georgiou_cdbp\n", | |
"mvn_activation\n", | |
"complex_signum\n", | |
"cart_sigmoid\n", | |
"cart_elu\n", | |
"cart_exponential\n", | |
"cart_hard_sigmoid\n", | |
"cart_relu\n", | |
"cart_leaky_relu\n", | |
"cart_selu\n", | |
"cart_softplus\n", | |
"cart_softsign\n", | |
"cart_tanh\n", | |
"cart_softmax\n", | |
"pol_tanh\n", | |
"pol_sigmoid\n", | |
"pol_selu\n", | |
"etf_circular_tan\n", | |
"etf_circular_sin\n", | |
"etf_inv_circular_atan\n", | |
"etf_inv_circular_asin\n", | |
"etf_inv_circular_acos\n", | |
"etf_circular_tanh\n", | |
"etf_circular_sinh\n", | |
"etf_inv_circular_atanh\n", | |
"etf_inv_circular_asinh\n", | |
"modrelu\n", | |
"crelu\n", | |
"zrelu\n" | |
], | |
"name": "stdout" | |
}, | |
{ | |
"output_type": "error", | |
"ename": "TypeError", | |
"evalue": "ignored", | |
"traceback": [ | |
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", | |
"\u001b[0;32m<ipython-input-5-49e907b0d470>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcvnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mComplexInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\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[1;32m 5\u001b[0m \u001b[0mcvnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mComplexDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mactivation\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----> 6\u001b[0;31m \u001b[0mcvnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mComplexDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'linear'\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 7\u001b[0m ])\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 521\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 522\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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 523\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 524\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, layers, name)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mlayers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\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 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 521\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 522\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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 523\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 524\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0;31m# If the model is being built continuously on top of an input layer:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;31m# refresh its output.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m \u001b[0moutput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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 229\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSINGLE_LAYER_OUTPUT_ERROR_MSG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 968\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\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 969\u001b[0m return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0;32m--> 970\u001b[0;31m input_list)\n\u001b[0m\u001b[1;32m 971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;31m# Maintains info about the `Layer.call` stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[0;31m# Check input assumptions set after layer building, e.g. input shape.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1107\u001b[0m outputs = self._keras_tensor_symbolic_call(\n\u001b[0;32m-> 1108\u001b[0;31m inputs, input_masks, args, kwargs)\n\u001b[0m\u001b[1;32m 1109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1110\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutputs\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/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_keras_tensor_symbolic_call\u001b[0;34m(self, inputs, input_masks, args, kwargs)\u001b[0m\n\u001b[1;32m 838\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeras_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKerasTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_signature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 839\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--> 840\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_infer_output_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\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 841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_infer_output_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\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/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_infer_output_signature\u001b[0;34m(self, inputs, args, kwargs, input_masks)\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;31m# overridden).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 877\u001b[0m \u001b[0;31m# TODO(kaftan): do we maybe_build here, or have we already done it?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 878\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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 879\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 880\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_maybe_build\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2623\u001b[0m \u001b[0;31m# operations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2624\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_init_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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-> 2625\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint:disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2626\u001b[0m \u001b[0;31m# We must set also ensure that the layer is marked as built, and the build\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2627\u001b[0m \u001b[0;31m# shape is stored since user defined build functions may not be calling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/cvnn/layers/core.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m 217\u001b[0m self.w_r = tf.Variable(\n\u001b[1;32m 218\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'kernel_r'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m \u001b[0minitial_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi_kernel_dtype\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 220\u001b[0m \u001b[0mtrainable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m )\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/cvnn/initializers.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, shape, dtype, **kwargs)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\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[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex64\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mfan_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfan_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_compute_fans\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[0;32m--> 103\u001b[0;31m \u001b[0marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_limit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfan_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfan_out\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 104\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/cvnn/initializers.py\u001b[0m in \u001b[0;36m_compute_limit\u001b[0;34m(self, fan_in, fan_out)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_compute_limit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfan_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfan_out\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--> 142\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6.\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfan_in\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfan_out\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 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'int'" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "MrRsFN5X0OrT" | |
}, | |
"source": [ | |
"" | |
], | |
"execution_count": null, | |
"outputs": [] | |
} | |
] | |
} |
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