Test_Analysis_MobileNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import models, layers, activations\n",
    "from tensorflow.keras.layers import Conv2D, TimeDistributed, BatchNormalization, MaxPooling2D, Flatten, Bidirectional, Dense,Dropout,Flatten\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 27, 64, 64, 3) (3000, 11)\n"
     ]
    }
   ],
   "source": [
    "x_data = np.load('lip/lip_x_data.npy') # x_save.npy\n",
    "y_data = np.load('lip/lip_y_data.npy') # y_data.npy\n",
    "y_data = keras.utils.to_categorical(y_data,11)\n",
    "print(x_data.shape, y_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (2400, 27, 64, 64, 3), X_test: (600, 27, 64, 64, 3)\n",
      "Y_train: (2400, 11), Y_test: (600, 11)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(x_data, y_data, test_size = 0.2, shuffle = True, stratify=y_data)\n",
    "print(\"x_train: {}, X_test: {}\".format(X_train.shape, X_test.shape))\n",
    "print(\"Y_train: {}, Y_test: {}\".format(Y_train.shape, Y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "from tensorflow.keras.layers import Input\n",
    "\n",
    "\n",
    "# this could also be the output a different Keras model or layer\n",
    "#input_tensor = Input(shape=(224, 224, 3))\n",
    "\n",
    "#model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True)\n",
    "\n",
    "class MobileNet_LSTM(keras.Model):\n",
    "    def __init__(self, num_classes):\n",
    "        super(MobileNet_LSTM, self).__init__()\n",
    "        #self.BZ = input_shape[0]\n",
    "        #self.frame = input_shape[1]\n",
    "\n",
    "        self.mobile = TimeDistributed(MobileNet(weights='imagenet', include_top=False))\n",
    "        #self.vgg.trainable = False\n",
    "\n",
    "        self.bilstm = layers.Bidirectional(layers.LSTM(256), merge_mode='concat')\n",
    "        self.dense = layers.Dense(num_classes, activation='softmax')\n",
    "        self.max = TimeDistributed(MaxPooling2D(pool_size=(2, 2)))\n",
    "        self.dropout = layers.Dropout(0.2)\n",
    "        self.flat= TimeDistributed(Flatten())\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.mobile(x)\n",
    "        x = self.max(x)\n",
    "        x = self.dropout(x)      \n",
    "        x = self.flat(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.bilstm(x)\n",
    "        return self.dense(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
     ]
    }
   ],
   "source": [
    "model = MobileNet_LSTM(num_classes = 11)\n",
    "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D1EDBB0> and <tensorflow.python.keras.layers.convolutional.ZeroPadding2D object at 0x000002719D1ED3D0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D1ED5B0> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D1EDBB0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D366400> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D205250>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D370C70> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D366400>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D3E6A90> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D3E6A30>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D405520> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D3E6A90>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D427040> and <tensorflow.python.keras.layers.convolutional.ZeroPadding2D object at 0x000002719D427CA0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D42F2E0> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D427040>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D45E4C0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D456F40>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D46E640> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D45E4C0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D4903D0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D490C10>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D497E80> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D4903D0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D4C84C0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D4C2610>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D4D7520> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D4C84C0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719B8F15B0> and <tensorflow.python.keras.layers.convolutional.ZeroPadding2D object at 0x000002719B7E10A0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D4EAF10> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719B8F15B0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D512790> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D512DC0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D528310> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D512790>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D549460> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D544DC0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D576F10> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D549460>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D57DCA0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D57D1F0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D58B5B0> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D57DCA0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D5D3940> and <tensorflow.python.keras.layers.convolutional.ZeroPadding2D object at 0x000002719D5B4250>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D5C5430> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D5D3940>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D5E6DF0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D5E6AC0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D5FD1F0> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D5E6DF0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D6209D0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D61A9A0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D64CF10> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D6209D0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D644EB0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D6440D0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D6620D0> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D644EB0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D68B040> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D687A30>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D6AB790> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D68B040>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D6BC160> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D6BC2B0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D6CF370> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D6BC160>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D6F2430> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D6F2580>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D7164F0> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D6F2430>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D7296D0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D701130>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D737880> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D7296D0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D75C520> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D75C940>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D760BB0> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D75C520>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D795880> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D78D7F0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D7A3820> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D795880>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D7C5370> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D7C5DC0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D7CDF10> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D7C5370>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D8010D0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D7F9640>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D80F400> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D8010D0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D832160> and <tensorflow.python.keras.layers.convolutional.ZeroPadding2D object at 0x000002719D832F10>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D839880> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D832160>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D86BBE0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D866D30>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D87D550> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719D86BBE0>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D89F310> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719D89FAF0>).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719D89F1C0> and <tensorflow.python.keras.layers.convolutional.DepthwiseConv2D object at 0x000002719D89F310>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719DA023A0> and <tensorflow.python.keras.layers.advanced_activations.ReLU object at 0x000002719DA02A00>).\n",
      "WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.\n",
      "\n",
      "Two checkpoint references resolved to different objects (<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x000002719DA172B0> and <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000002719DA023A0>).\n",
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "model.load_weights(\"mobileNet_lip\") \n",
    "print(\"Loaded model from disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 27, 64, 64, 3) (600, 11)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#score = model.evaluate(X_test, Y_test, batch_size = 32, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a1,a2,a3,a4, a5, a6, a7, a8, a9, a10 = [], [], [],[],[],[],[],[],[],[]\n",
    "# for i in range(len(Y_test)):\n",
    "#     y = np.argmax(Y_test[i])\n",
    "#     if y == 1:\n",
    "#         a1.append(i)\n",
    "#     elif y==2:\n",
    "#         a2.append(i)\n",
    "#     elif y== 3:\n",
    "#         a3.append(i)\n",
    "#     elif y==4:\n",
    "#         a4.append(i)\n",
    "#     elif y==5:\n",
    "#         a5.append(i)\n",
    "#     elif y== 6:\n",
    "#         a6.append(i)\n",
    "#     elif y==7:\n",
    "#         a7.append(i)\n",
    "#     elif y==8:\n",
    "#         a8.append(i)\n",
    "#     elif y==9:\n",
    "#         a9.append(i)\n",
    "#     elif y==10:\n",
    "#         a10.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 600개의 X_test에 대한 결과 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 각 결과에 대한 정답 비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_test의 index 1~10 까지 60개씩 존재 : 총 600개\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#정답 비율 확인\n",
    "answer = []\n",
    "no_answer =[]\n",
    "\n",
    "for i in range(600):\n",
    "    true_label  = np.argmax(Y_test[i])\n",
    "    predictions_array  = np.argmax(predictions[i])  \n",
    "    if true_label == predictions_array:\n",
    "        answer.append(true_label)\n",
    "    else:\n",
    "        no_answer.append(true_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "552 48\n"
     ]
    }
   ],
   "source": [
    "print(len(answer), len(no_answer)) # 76, 524"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "per = []\n",
    "for i in range(1, 11):\n",
    "    per.append(answer.count(i)/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_words = ['Begin', 'Choose','Connection', 'Navigation', 'Next','Previous','Start', 'Stop', 'Hello', 'Web']\n",
    "class_sen = ['Stop navigation', 'Excuse me.', 'I am sorry.', 'Thank you.', 'Good bye.', 'I love this game.', 'Nice to meet you.', 'You are welcome.', 'How are you?', 'Have a good time.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.title('Percentage of correct answers for each sentence')\n",
    "\n",
    "y = np.arange(10)\n",
    "plt.bar(y, per)\n",
    "plt.xticks(y, class_sen, rotation = 45)\n",
    "plt.show()\n",
    "#plt.bar(y, per, align='edge', color=\"skyblue\", edgecolor=\"black\", linewidth=3, tick_label=y, log=False)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm= confusion_matrix(np.argmax(Y_test, axis=1),np.argmax(predictions, axis=1))\n",
    "fig, ax = plt.subplots(figsize = (10, 10))\n",
    "im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "ax.figure.colorbar(im, ax=ax)\n",
    "ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=class_sen, yticklabels=class_sen, title='Percentage of correct answers for each sentence',\n",
    "           ylabel='True label', xlabel='Predicted label')\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "fmt = '.2f' \n",
    "thresh = cm.max() / 2.\n",
    "for i in range(cm.shape[0]):\n",
    "    for j in range(cm.shape[1]):\n",
    "        ax.text(j, i, format(cm[i, j], fmt), fontsize = 10,ha=\"center\", va=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.9963430e-07, 2.7242128e-04, 7.0647836e-02, 2.1426266e-03,\n",
       "       6.0913253e-01, 2.1670118e-04, 5.2681244e-03, 3.0913952e-01,\n",
       "       1.8262552e-06, 8.8243285e-04, 2.2951432e-03], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0] # 첫번째 예측 (10개의 숫자배열)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0]) # 가장 높은 신뢰도를 가진 레이블 (10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.8726459e-08, 1.7875159e-07, 3.1360396e-05, 9.3869940e-06,\n",
       "       7.4710607e-07, 4.2930522e-05, 6.4284022e-06, 3.0544680e-04,\n",
       "       6.8709898e-07, 5.2064177e-05, 9.9955076e-01], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[599]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i = 0\n",
    "# predictions_array, true_label = predictions[i], Y_test[i]\n",
    "# true_label = np.argmax(true_label)\n",
    "# predicted_label = np.argmax(predictions_array)\n",
    "# if predicted_label == true_label:\n",
    "#     color = 'blue'\n",
    "# else:\n",
    "#     color = 'red'\n",
    "\n",
    "# #print(\"{} {:2.0f}% ({})\".format(class_sen[predicted_label], 100*np.max(predictions_array), class_sen[true_label]), color=color)\n",
    "# print(\"{} {:2.0f}% ({})\".format(class_sen[predicted_label], 100*np.max(predictions_array), class_sen[true_label]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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