note_h5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 12)                156       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 58)                754       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 60)                3540      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 57)                3477      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 57)                3306      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 56)                3248      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,481\n",
      "Trainable params: 14,481\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 12),\n",
       "    'dtype': 'int32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'dense_input'}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 12,\n",
       "    'activation': 'swish',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_1',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 58,\n",
       "    'activation': 'swish',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_2',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 60,\n",
       "    'activation': 'swish',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_3',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 57,\n",
       "    'activation': 'softplus',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_4',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 57,\n",
       "    'activation': 'softplus',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_5',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 56,\n",
       "    'activation': 'softmax',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "     'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}}]}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FILE = 'ep335_loss0.285_acc0.965_vloss0.278_vacc0.972.h5'\n",
    "\n",
    "model = tf.keras.models.load_model(FILE)\n",
    "model.summary()\n",
    "display(model.get_config())\n"
   ]
  }
 ],
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