ML4DM_Lab_3_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML4DM_Lab_3 - 2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nishi1612/Machine-Learning-For-Data-Mining/blob/master/ML4DM_Lab_3_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5VpezvpYMqz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from keras.datasets import cifar10\n",
        "import keras\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KX9KjmINYTe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "((trainX, trainY), (testX, testY)) = cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fj-KM5qTYgcF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "20b87711-e256-428d-b995-3c915427f676"
      },
      "source": [
        "trainX.shape , trainY.shape, testX.shape, testY.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 32, 32, 3), (50000, 1), (10000, 32, 32, 3), (10000, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBXZz9WZYjD8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainX = trainX/255.0\n",
        "testX = testX/255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYf7yOpMYjsU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_graph(history):\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.title('Loss')\n",
        "  plt.xlabel('No. of epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.show()\n",
        "  plt.plot(history.history['acc'])\n",
        "  plt.title('Accuracy')\n",
        "  plt.xlabel('No. of epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlyS5_MAYlM_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs = 10\n",
        "verbose = 1\n",
        "validation_split = 0.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDpSfMLJYmp_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "69560b54-61df-476b-8328-df091d031fb6"
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(trainX.shape[1],trainX.shape[2],trainX.shape[3])),\n",
        "    keras.layers.Dense(512,activation = tf.nn.relu),\n",
        "    keras.layers.Dense(10,activation=tf.nn.softmax)    \n",
        "])\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3622: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 3072)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1573376   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,578,506\n",
            "Trainable params: 1,578,506\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeMkxpRRYo-S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "d1bca28f-68a0-4781-cef0-39797f4a4512"
      },
      "source": [
        "history = model.fit(trainX, trainY, epochs=epochs, validation_split=validation_split,verbose=verbose, shuffle=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "40000/40000 [==============================] - 40s 989us/step - loss: 1.9078 - acc: 0.3166 - val_loss: 1.7945 - val_acc: 0.3519\n",
            "Epoch 2/10\n",
            "40000/40000 [==============================] - 34s 859us/step - loss: 1.7303 - acc: 0.3794 - val_loss: 1.7368 - val_acc: 0.3798\n",
            "Epoch 3/10\n",
            "40000/40000 [==============================] - 34s 857us/step - loss: 1.6722 - acc: 0.4017 - val_loss: 1.7091 - val_acc: 0.3977\n",
            "Epoch 4/10\n",
            "40000/40000 [==============================] - 35s 870us/step - loss: 1.6283 - acc: 0.4197 - val_loss: 1.6727 - val_acc: 0.4073\n",
            "Epoch 5/10\n",
            "40000/40000 [==============================] - 35s 871us/step - loss: 1.5950 - acc: 0.4319 - val_loss: 1.6262 - val_acc: 0.4209\n",
            "Epoch 6/10\n",
            "40000/40000 [==============================] - 35s 867us/step - loss: 1.5638 - acc: 0.4449 - val_loss: 1.6113 - val_acc: 0.4256\n",
            "Epoch 7/10\n",
            "40000/40000 [==============================] - 40s 1ms/step - loss: 1.5488 - acc: 0.4495 - val_loss: 1.5994 - val_acc: 0.4370\n",
            "Epoch 8/10\n",
            "40000/40000 [==============================] - 41s 1ms/step - loss: 1.5265 - acc: 0.4596 - val_loss: 1.5759 - val_acc: 0.4373\n",
            "Epoch 9/10\n",
            "40000/40000 [==============================] - 43s 1ms/step - loss: 1.5181 - acc: 0.4626 - val_loss: 1.5930 - val_acc: 0.4317\n",
            "Epoch 10/10\n",
            "40000/40000 [==============================] - 42s 1ms/step - loss: 1.5032 - acc: 0.4641 - val_loss: 1.5521 - val_acc: 0.4536\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfiuNDyqZCHm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "b71460d6-a227-4fc1-c566-9ed3b7fd293f"
      },
      "source": [
        "show_graph(history)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Epw7JPD79fC45K5e756ziu8+UUNfQGHZZIiIfUGDEkYyOKfz2llF8YcLp/PGNDUz9/RJ2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\nREWBISIiUVFgiIhIVP4/MteuXosm/N0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2Ew4L6zA2V9Vw91/LuO3ZZby/pZopY/tx09kHcez4Ab5OwiwHh4W1exu3VnPH8+9w5wvv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\neUqAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKhei3hMZCuC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4b100b30-d81c-43a1-d20d-22d174ba51c6"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(testX, testY)\n",
        "test_loss , test_acc"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 152us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.5353830251693725, 0.4559)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iloo1xHfZFz2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "3fec8abc-457e-4ee6-dd40-33af276b3c0b"
      },
      "source": [
        "model_1 = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(trainX.shape[1],trainX.shape[2],trainX.shape[3])),\n",
        "    keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(256, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(64, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model_1.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model_1.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_4 (Flatten)          (None, 3072)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 512)               1573376   \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 1,746,506\n",
            "Trainable params: 1,746,506\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXd1mmDYZOIk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "ff56588b-bbc9-4a6b-c943-496d4f149597"
      },
      "source": [
        "history_1 = model_1.fit(trainX, trainY, epochs=epochs, validation_split=validation_split,verbose=verbose, shuffle=True)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "40000/40000 [==============================] - 42s 1ms/step - loss: 1.9098 - acc: 0.3002 - val_loss: 1.7728 - val_acc: 0.3609\n",
            "Epoch 2/10\n",
            "40000/40000 [==============================] - 40s 1ms/step - loss: 1.7222 - acc: 0.3808 - val_loss: 1.7212 - val_acc: 0.3783\n",
            "Epoch 3/10\n",
            "40000/40000 [==============================] - 40s 1ms/step - loss: 1.6456 - acc: 0.4078 - val_loss: 1.6321 - val_acc: 0.4165\n",
            "Epoch 4/10\n",
            "40000/40000 [==============================] - 46s 1ms/step - loss: 1.5836 - acc: 0.4316 - val_loss: 1.6327 - val_acc: 0.4186\n",
            "Epoch 5/10\n",
            "40000/40000 [==============================] - 42s 1ms/step - loss: 1.5411 - acc: 0.4461 - val_loss: 1.6321 - val_acc: 0.4247\n",
            "Epoch 6/10\n",
            "40000/40000 [==============================] - 40s 1ms/step - loss: 1.5005 - acc: 0.4596 - val_loss: 1.5428 - val_acc: 0.4501\n",
            "Epoch 7/10\n",
            "40000/40000 [==============================] - 42s 1ms/step - loss: 1.4727 - acc: 0.4703 - val_loss: 1.5312 - val_acc: 0.4593\n",
            "Epoch 8/10\n",
            "40000/40000 [==============================] - 41s 1ms/step - loss: 1.4469 - acc: 0.4799 - val_loss: 1.5180 - val_acc: 0.4662\n",
            "Epoch 9/10\n",
            "40000/40000 [==============================] - 41s 1ms/step - loss: 1.4168 - acc: 0.4893 - val_loss: 1.5411 - val_acc: 0.4484\n",
            "Epoch 10/10\n",
            "40000/40000 [==============================] - 40s 1ms/step - loss: 1.3994 - acc: 0.4944 - val_loss: 1.6055 - val_acc: 0.4431\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0mtvbt8ZQWg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "8e7b58dd-0b19-4d63-a268-c9fb896495b8"
      },
      "source": [
        "show_graph(history_1)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hbHKRrurWnlceHUn71i346owP+NPa/UFHEpEoUd+7jxaaWUp4usyPgF+b2U8jG00uRfcO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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zQu6fWcLpwwv5xeXH06W958sys0OrLomkTNJFNU+iS5oArM9tWFabDVt38fkp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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWkJqDZsZXzd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7b0b6a93-0285-4fde-f29a-c325fe2b8592"
      },
      "source": [
        "test_loss_1, test_acc_1 = model_1.evaluate(testX, testY)\n",
        "test_loss_1 , test_acc_1"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 184us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.5771126375198363, 0.4466)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yXaaGtkZZ9w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "84c273f8-e168-4974-9395-c38748528cd6"
      },
      "source": [
        "model_2 = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(trainX.shape[1],trainX.shape[2],trainX.shape[3])),\n",
        "    keras.layers.Dense(512, activation=tf.nn.tanh),\n",
        "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model_2.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model_2.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_5 (Flatten)          (None, 3072)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 512)               1573376   \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,578,506\n",
            "Trainable params: 1,578,506\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6B_z_jomZb9t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "ffbaaf5d-5112-45c1-d6e8-e3add406cb64"
      },
      "source": [
        "history_2 = model_2.fit(trainX, trainY, epochs=epochs, validation_split=validation_split,verbose=verbose, shuffle=True)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "40000/40000 [==============================] - 44s 1ms/step - loss: 2.4401 - acc: 0.2672 - val_loss: 1.9223 - val_acc: 0.3070\n",
            "Epoch 2/10\n",
            "40000/40000 [==============================] - 45s 1ms/step - loss: 1.8923 - acc: 0.3151 - val_loss: 1.9265 - val_acc: 0.3248\n",
            "Epoch 3/10\n",
            "40000/40000 [==============================] - 45s 1ms/step - loss: 1.8613 - acc: 0.3268 - val_loss: 1.8647 - val_acc: 0.3237\n",
            "Epoch 4/10\n",
            "40000/40000 [==============================] - 43s 1ms/step - loss: 1.8468 - acc: 0.3343 - val_loss: 1.8564 - val_acc: 0.3258\n",
            "Epoch 5/10\n",
            "40000/40000 [==============================] - 43s 1ms/step - loss: 1.8449 - acc: 0.3341 - val_loss: 1.8150 - val_acc: 0.3442\n",
            "Epoch 6/10\n",
            "40000/40000 [==============================] - 44s 1ms/step - loss: 1.8346 - acc: 0.3392 - val_loss: 1.8355 - val_acc: 0.3490\n",
            "Epoch 7/10\n",
            "40000/40000 [==============================] - 43s 1ms/step - loss: 1.8267 - acc: 0.3459 - val_loss: 1.8583 - val_acc: 0.3349\n",
            "Epoch 8/10\n",
            "40000/40000 [==============================] - 43s 1ms/step - loss: 1.8514 - acc: 0.3387 - val_loss: 1.8755 - val_acc: 0.3298\n",
            "Epoch 9/10\n",
            "40000/40000 [==============================] - 42s 1ms/step - loss: 1.8335 - acc: 0.3400 - val_loss: 1.8502 - val_acc: 0.3365\n",
            "Epoch 10/10\n",
            "40000/40000 [==============================] - 42s 1ms/step - loss: 1.8277 - acc: 0.3468 - val_loss: 1.8615 - val_acc: 0.3504\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xm_oQ1S3Zg1O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "e0292da7-24da-4965-d57c-7b5a6df456b2"
      },
      "source": [
        "show_graph(history_2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ovaSDwC8DVQAR8RngV4E7JT0JCPiFiFjpSWxmtqIlFgoR8aPnePww8ANJvb+Z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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HxFYASV0kDY2IRRmtrJ0pSWaZ9fUKM2uLmnPN4hGgPm25LmmzvTCjrJoDe3Zi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sm4ne-SEZjHk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c1237470-b981-4bde-db6f-415893ab4257"
      },
      "source": [
        "test_loss_2, test_acc_2 = model_2.evaluate(testX, testY)\n",
        "test_loss_2, test_acc_2"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 156us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.825080970954895, 0.3644)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    }
  ]
}