ASL_model_train.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import models, layers\n",
        "from tensorflow.keras.regularizers import l2,l1_l2,l1\n",
        "import pickle as pkl"
      ],
      "metadata": {
        "id": "lDG5Qbh5ordh"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint"
      ],
      "metadata": {
        "id": "q_aJ4gvElGCx"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "complete_data = {}\n",
        "with open('data_2.pkl','rb') as handle:\n",
        "  complete_data = pkl.load(handle)"
      ],
      "metadata": {
        "id": "sJMqnlu8o7bM"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "complete_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJqnv3J9pKIx",
        "outputId": "f9a6977a-1e08-44ee-a9cb-2d5014f8f88d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'training': array([[[169, 174, 169, ..., 170, 170, 167],\n",
              "         [169, 175, 171, ..., 171, 170, 169],\n",
              "         [169, 175, 171, ..., 170, 168, 168],\n",
              "         ...,\n",
              "         [140, 133, 118, ..., 118, 120, 120],\n",
              "         [134, 127, 115, ..., 120, 118, 116],\n",
              "         [131, 127, 115, ..., 116, 117, 118]],\n",
              " \n",
              "        [[172,  66, 146, ..., 140, 139, 139],\n",
              "         [171,  63, 150, ..., 158, 148, 138],\n",
              "         [167,  59, 151, ..., 156, 146, 133],\n",
              "         ...,\n",
              "         [100,  35,  80, ..., 169, 146, 186],\n",
              "         [103,  36, 114, ..., 196, 147, 166],\n",
              "         [104,  50, 130, ..., 185, 165, 144]],\n",
              " \n",
              "        [[143, 139, 133, ..., 132, 126, 133],\n",
              "         [141, 137, 135, ..., 136, 153, 151],\n",
              "         [139, 137, 113, ..., 157, 157, 152],\n",
              "         ...,\n",
              "         [110, 114, 100, ..., 120, 120, 121],\n",
              "         [108, 109, 109, ..., 114, 113, 114],\n",
              "         [106, 111, 105, ...,  93, 109, 109]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[ 40,  70,  87, ..., 180, 170, 170],\n",
              "         [ 34, 101,  94, ..., 175, 173, 156],\n",
              "         [ 33,  97,  99, ..., 178, 178, 180],\n",
              "         ...,\n",
              "         [ 18,  13,  14, ..., 109, 146, 146],\n",
              "         [ 21,  18,  15, ...,  85,  85, 100],\n",
              "         [ 21,  20,  19, ..., 122,  95, 109]],\n",
              " \n",
              "        [[ 86, 168, 168, ..., 173, 174, 173],\n",
              "         [ 86, 167, 168, ..., 173, 174, 174],\n",
              "         [ 90, 166, 165, ..., 173, 172, 172],\n",
              "         ...,\n",
              "         [ 88, 143, 141, ..., 120, 121, 121],\n",
              "         [113, 139, 137, ..., 121, 118, 119],\n",
              "         [144, 134, 133, ..., 119, 117, 115]],\n",
              " \n",
              "        [[177, 176, 177, ..., 207, 212, 182],\n",
              "         [176, 174, 174, ..., 208, 205, 184],\n",
              "         [174, 175, 174, ..., 204, 212, 159],\n",
              "         ...,\n",
              "         [178, 166, 162, ...,  73,  86, 109],\n",
              "         [162, 187, 158, ...,  70,  81,  98],\n",
              "         [162, 172, 151, ...,  69,  76,  91]]], dtype=uint8),\n",
              " 'testing': array([[[ 41,  50, 111, ..., 189, 190, 191],\n",
              "         [ 41,  47, 111, ..., 188, 185, 183],\n",
              "         [ 44,  80, 122, ..., 177, 174, 178],\n",
              "         ...,\n",
              "         [ 39,  43,  46, ..., 138, 144, 143],\n",
              "         [ 35,  34,  35, ..., 137, 137, 147],\n",
              "         [ 29,  30,  32, ..., 109, 109, 109]],\n",
              " \n",
              "        [[164, 166, 166, ..., 137, 139, 137],\n",
              "         [167, 166, 165, ..., 138, 138, 137],\n",
              "         [167, 168, 166, ..., 136, 136, 135],\n",
              "         ...,\n",
              "         [135, 109,  72, ..., 111, 109, 110],\n",
              "         [134, 124,  83, ..., 109, 108, 109],\n",
              "         [136, 131,  99, ..., 109, 111, 109]],\n",
              " \n",
              "        [[159, 168, 169, ..., 201, 209, 186],\n",
              "         [156, 174, 173, ..., 204, 201, 185],\n",
              "         [175, 169, 181, ..., 200, 207, 184],\n",
              "         ...,\n",
              "         [121,  81,  88, ...,  83, 102, 119],\n",
              "         [ 95,  81,  83, ...,  81,  98, 114],\n",
              "         [ 73,  82,  80, ...,  73,  97, 117]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[169, 169, 169, ..., 142, 141, 142],\n",
              "         [169, 171, 171, ..., 141, 140, 138],\n",
              "         [170, 172, 172, ..., 140, 138, 137],\n",
              "         ...,\n",
              "         [144, 143, 143, ..., 125, 106, 115],\n",
              "         [141, 141, 141, ..., 117, 101, 111],\n",
              "         [141, 141, 140, ..., 122, 100, 109]],\n",
              " \n",
              "        [[106, 116, 117, ..., 173, 183, 146],\n",
              "         [ 96, 103,  98, ..., 177, 176, 185],\n",
              "         [ 88,  85,  95, ..., 170, 172, 174],\n",
              "         ...,\n",
              "         [109,  92,  79, ..., 162, 165, 163],\n",
              "         [120, 100,  91, ..., 142, 157, 158],\n",
              "         [152, 104, 111, ..., 131, 150, 155]],\n",
              " \n",
              "        [[ 57,  57,  58, ...,  65,  66,  68],\n",
              "         [ 58,  58,  58, ...,  63,  67,  67],\n",
              "         [ 59,  57,  61, ...,  67,  69,  67],\n",
              "         ...,\n",
              "         [ 47,  49,  48, ...,  90, 120, 168],\n",
              "         [ 47,  50,  47, ..., 114, 118, 119],\n",
              "         [ 45,  47,  46, ..., 127, 133, 139]]], dtype=uint8),\n",
              " 'train_output': array([4, 5, 0, ..., 2, 4, 0], dtype=int32),\n",
              " 'test_output': array([2, 1, 3, ..., 1, 1, 1], dtype=int32)}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = complete_data['training']/255\n",
        "x_test = complete_data['testing']/255\n",
        "y_train = complete_data['train_output']\n",
        "y_test = complete_data['test_output']"
      ],
      "metadata": {
        "id": "cwYZU1lBpuYo"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-km_VFXmaPT",
        "outputId": "223bab64-25f0-4bef-a27e-d880fc154129"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33604, 32, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_validation = x_train[25000:]\n",
        "y_validation = y_train[25000:]"
      ],
      "metadata": {
        "id": "cpXxzZW2qCDT"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = models.Sequential()\n",
        "model1.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32,1),kernel_regularizer=l2(0.0002)))\n",
        "model1.add(layers.MaxPooling2D((2, 2)))\n",
        "# model1.add(layers.AveragePooling2D((2,2)))\n",
        "model1.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model1.add(layers.MaxPooling2D((2, 2)))\n",
        "# model1.add(layers.AveragePooling2D((2,2)))\n",
        "model1.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "model1.add(layers.MaxPooling2D((2, 2)))\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xywAZGXvqHti",
        "outputId": "40720afb-eeab-4f31-f322-a6bed666ec9c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 30, 30, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 92,672\n",
            "Trainable params: 92,672\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.add(layers.Flatten())\n",
        "model1.add(layers.Dropout(0.2))\n",
        "model1.add(layers.Dense(256, activation='relu'))\n",
        "model1.add(layers.Dense(6,activation='softmax'))\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7HXvYxtqLz9",
        "outputId": "d3c1475b-79a9-4c7a-d7bf-bcbe09c51a75"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 30, 30, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 1542      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 225,542\n",
            "Trainable params: 225,542\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# model1.compile(loss='categorical_crossentropy',\n",
        "#               optimizer=tf.keras.optimizers.RMSprop(lr=1e-4),\n",
        "#               metrics=['acc'])\n",
        "\n",
        "filename='asl_model2.h5'\n",
        "checkpoint= ModelCheckpoint(filename,monitor='val_loss',verbose=1,save_best_only=True,mode='min')\n",
        "\n",
        "history = model1.fit(x_train, y_train, epochs=50,validation_data=(x_validation,y_validation),batch_size=512,callbacks=[checkpoint])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_h739X-pqQLO",
        "outputId": "068e3a9d-21be-4ad5-c9f7-aecc552927ca"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.0558 - accuracy: 0.6226\n",
            "Epoch 1: val_loss improved from inf to 0.42894, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 9s 23ms/step - loss: 1.0558 - accuracy: 0.6226 - val_loss: 0.4289 - val_accuracy: 0.8634\n",
            "Epoch 2/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.2722 - accuracy: 0.9174\n",
            "Epoch 2: val_loss improved from 0.42894 to 0.14712, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.2712 - accuracy: 0.9177 - val_loss: 0.1471 - val_accuracy: 0.9587\n",
            "Epoch 3/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.9633\n",
            "Epoch 3: val_loss improved from 0.14712 to 0.07730, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.1240 - accuracy: 0.9634 - val_loss: 0.0773 - val_accuracy: 0.9801\n",
            "Epoch 4/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0814 - accuracy: 0.9772\n",
            "Epoch 4: val_loss improved from 0.07730 to 0.05140, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0817 - accuracy: 0.9771 - val_loss: 0.0514 - val_accuracy: 0.9872\n",
            "Epoch 5/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0563 - accuracy: 0.9849\n",
            "Epoch 5: val_loss improved from 0.05140 to 0.04203, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0563 - accuracy: 0.9849 - val_loss: 0.0420 - val_accuracy: 0.9886\n",
            "Epoch 6/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0438 - accuracy: 0.9884\n",
            "Epoch 6: val_loss improved from 0.04203 to 0.02975, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0438 - accuracy: 0.9884 - val_loss: 0.0298 - val_accuracy: 0.9913\n",
            "Epoch 7/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0340 - accuracy: 0.9900\n",
            "Epoch 7: val_loss improved from 0.02975 to 0.02032, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0339 - accuracy: 0.9901 - val_loss: 0.0203 - val_accuracy: 0.9944\n",
            "Epoch 8/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0267 - accuracy: 0.9925\n",
            "Epoch 8: val_loss improved from 0.02032 to 0.01343, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0268 - accuracy: 0.9924 - val_loss: 0.0134 - val_accuracy: 0.9956\n",
            "Epoch 9/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0208 - accuracy: 0.9941\n",
            "Epoch 9: val_loss improved from 0.01343 to 0.01296, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0207 - accuracy: 0.9942 - val_loss: 0.0130 - val_accuracy: 0.9966\n",
            "Epoch 10/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0192 - accuracy: 0.9946\n",
            "Epoch 10: val_loss improved from 0.01296 to 0.00951, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0192 - accuracy: 0.9946 - val_loss: 0.0095 - val_accuracy: 0.9978\n",
            "Epoch 11/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0145 - accuracy: 0.9961\n",
            "Epoch 11: val_loss improved from 0.00951 to 0.00794, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.0079 - val_accuracy: 0.9984\n",
            "Epoch 12/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0117 - accuracy: 0.9969\n",
            "Epoch 12: val_loss improved from 0.00794 to 0.00536, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 0.0054 - val_accuracy: 0.9991\n",
            "Epoch 13/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0106 - accuracy: 0.9972\n",
            "Epoch 13: val_loss improved from 0.00536 to 0.00370, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.0037 - val_accuracy: 0.9995\n",
            "Epoch 14/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0079 - accuracy: 0.9979\n",
            "Epoch 14: val_loss improved from 0.00370 to 0.00270, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0079 - accuracy: 0.9979 - val_loss: 0.0027 - val_accuracy: 0.9997\n",
            "Epoch 15/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0078 - accuracy: 0.9980\n",
            "Epoch 15: val_loss did not improve from 0.00270\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0078 - accuracy: 0.9980 - val_loss: 0.0033 - val_accuracy: 0.9993\n",
            "Epoch 16/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0069 - accuracy: 0.9980\n",
            "Epoch 16: val_loss improved from 0.00270 to 0.00194, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0069 - accuracy: 0.9981 - val_loss: 0.0019 - val_accuracy: 0.9999\n",
            "Epoch 17/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0079 - accuracy: 0.9975\n",
            "Epoch 17: val_loss improved from 0.00194 to 0.00184, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0078 - accuracy: 0.9975 - val_loss: 0.0018 - val_accuracy: 0.9998\n",
            "Epoch 18/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0055 - accuracy: 0.9986\n",
            "Epoch 18: val_loss did not improve from 0.00184\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0056 - accuracy: 0.9985 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0045 - accuracy: 0.9989\n",
            "Epoch 19: val_loss did not improve from 0.00184\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0045 - accuracy: 0.9990 - val_loss: 0.0037 - val_accuracy: 0.9988\n",
            "Epoch 20/50\n",
            "64/66 [============================>.] - ETA: 0s - loss: 0.0053 - accuracy: 0.9987\n",
            "Epoch 20: val_loss did not improve from 0.00184\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0052 - accuracy: 0.9987 - val_loss: 0.0025 - val_accuracy: 0.9998\n",
            "Epoch 21/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0040 - accuracy: 0.9992\n",
            "Epoch 21: val_loss did not improve from 0.00184\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0040 - accuracy: 0.9992 - val_loss: 0.0020 - val_accuracy: 0.9999\n",
            "Epoch 22/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0050 - accuracy: 0.9989\n",
            "Epoch 22: val_loss did not improve from 0.00184\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.0038 - val_accuracy: 0.9994\n",
            "Epoch 23/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0031 - accuracy: 0.9993\n",
            "Epoch 23: val_loss improved from 0.00184 to 0.00121, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0031 - accuracy: 0.9993\n",
            "Epoch 24: val_loss improved from 0.00121 to 0.00113, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0031 - accuracy: 0.9993 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0042 - accuracy: 0.9990\n",
            "Epoch 25: val_loss improved from 0.00113 to 0.00110, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0029 - accuracy: 0.9992\n",
            "Epoch 26: val_loss did not improve from 0.00110\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 0.0011 - val_accuracy: 0.9998\n",
            "Epoch 27/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0027 - accuracy: 0.9995\n",
            "Epoch 27: val_loss improved from 0.00110 to 0.00091, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 9.1057e-04 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "64/66 [============================>.] - ETA: 0s - loss: 0.0026 - accuracy: 0.9995\n",
            "Epoch 28: val_loss improved from 0.00091 to 0.00087, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 8.6809e-04 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0035 - accuracy: 0.9992\n",
            "Epoch 29: val_loss did not improve from 0.00087\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.0029 - val_accuracy: 0.9992\n",
            "Epoch 30/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0032 - accuracy: 0.9993\n",
            "Epoch 30: val_loss did not improve from 0.00087\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 0.0013 - val_accuracy: 0.9998\n",
            "Epoch 31/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0018 - accuracy: 0.9998\n",
            "Epoch 31: val_loss improved from 0.00087 to 0.00073, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 7.2774e-04 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0021 - accuracy: 0.9995\n",
            "Epoch 32: val_loss did not improve from 0.00073\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 7.5413e-04 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0021 - accuracy: 0.9996\n",
            "Epoch 33: val_loss improved from 0.00073 to 0.00069, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 6.9435e-04 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0018 - accuracy: 0.9998\n",
            "Epoch 34: val_loss did not improve from 0.00069\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0018 - accuracy: 0.9999 - val_loss: 7.6631e-04 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0012 - accuracy: 0.9999\n",
            "Epoch 35: val_loss improved from 0.00069 to 0.00067, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 6.6701e-04 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0014 - accuracy: 0.9998\n",
            "Epoch 36: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 8.8078e-04 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0024 - accuracy: 0.9994\n",
            "Epoch 37: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 9.6768e-04 - val_accuracy: 0.9999\n",
            "Epoch 38/50\n",
            "63/66 [===========================>..] - ETA: 0s - loss: 0.0015 - accuracy: 0.9998\n",
            "Epoch 38: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 18ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 6.6857e-04 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0013 - accuracy: 0.9999\n",
            "Epoch 39: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 7.2661e-04 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0031 - accuracy: 0.9993\n",
            "Epoch 40: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0031 - accuracy: 0.9993 - val_loss: 0.0048 - val_accuracy: 0.9985\n",
            "Epoch 41/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0026 - accuracy: 0.9994\n",
            "Epoch 41: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 8.8668e-04 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0014 - accuracy: 0.9998\n",
            "Epoch 42: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 7.3849e-04 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0011 - accuracy: 0.9999\n",
            "Epoch 43: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 8.1689e-04 - val_accuracy: 0.9999\n",
            "Epoch 44/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0018 - accuracy: 0.9996\n",
            "Epoch 44: val_loss did not improve from 0.00067\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0011 - val_accuracy: 0.9999\n",
            "Epoch 45/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0024 - accuracy: 0.9995\n",
            "Epoch 45: val_loss improved from 0.00067 to 0.00067, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 6.6547e-04 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0014 - accuracy: 0.9999\n",
            "Epoch 46: val_loss improved from 0.00067 to 0.00064, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 6.4162e-04 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0011 - accuracy: 0.9998\n",
            "Epoch 47: val_loss did not improve from 0.00064\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 6.6505e-04 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0010 - accuracy: 0.9999\n",
            "Epoch 48: val_loss improved from 0.00064 to 0.00060, saving model to asl_model2.h5\n",
            "66/66 [==============================] - 1s 17ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 6.0399e-04 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0013 - accuracy: 0.9997\n",
            "Epoch 49: val_loss did not improve from 0.00060\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 7.0859e-04 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "65/66 [============================>.] - ETA: 0s - loss: 0.0013 - accuracy: 0.9998\n",
            "Epoch 50: val_loss did not improve from 0.00060\n",
            "66/66 [==============================] - 1s 16ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 6.8601e-04 - val_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "D3QilTuWnw_Z"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.legend(['train','validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "LamTVegzqVuu",
        "outputId": "a96a248c-2fb5-4f65-ddfb-3ebb849e0355"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XHVRi7IinpGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models.save_model(model1,'asl_model2.h5')"
      ],
      "metadata": {
        "id": "kgiK1fRWqhTG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "4VNUoK7Kqlai"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "jBYRM80qsKvk"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(model1.predict(np.array([x_test[25]])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3PWpXMYroJ5",
        "outputId": "a6fec474-7c48-457e-d3c9-650b779df8e1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 15ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[25]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSx7XJZ0sJBh",
        "outputId": "5bd24d68-85f8-4ad8-e0e1-8edc4c6828e4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_6kOpKfrynD",
        "outputId": "e92c5eac-fc40-4480-c906-843c7a1d3ec9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8402, 32, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0nnGORwslfZ",
        "outputId": "97f9c635-60af-4cf8-b33f-ea0a236c234c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2, 3, 4, 5}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total = 0\n",
        "correct = 0\n",
        "for i in range(x_test.shape[0]):\n",
        "  total+=1\n",
        "  if np.argmax(model1.predict(np.array([x_test[i]]))) == y_test[i]:\n",
        "    correct+=1\n",
        "print(f\"Accuracy: {correct*100/total}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bDMOYp1msoT5",
        "outputId": "ec45ef00-7fe2-40b8-ea6e-0df4d9d6832b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
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            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "Accuracy: 99.98809807188765\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0EVIyBGp9lOs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}