day1/session3/example1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"example1.ipynb","provenance":[],"authorship_tag":"ABX9TyMbgqa3gQ/VTUnSo6R1SHxM"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"6SEaUopfhuqq","colab_type":"text"},"source":["# Imports"]},{"cell_type":"code","metadata":{"id":"BF1Clk-4d6vI","colab_type":"code","colab":{}},"source":["import pandas as pd\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense\n","from tensorflow.keras import optimizers\n","\n","%matplotlib inline  \n","import matplotlib.pyplot as plt"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YUfBCBlCiCbp","colab_type":"text"},"source":["# Gather Data"]},{"cell_type":"code","metadata":{"id":"QASUd96KeA_2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":208},"outputId":"7d7702a2-b8b3-4add-fc6a-8277a0c65d3c","executionInfo":{"status":"ok","timestamp":1588447638294,"user_tz":240,"elapsed":3092,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# Download the data file from UCI\n","! wget https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"],"execution_count":2,"outputs":[{"output_type":"stream","text":["--2020-05-02 19:27:17--  https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n","Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n","Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 4551 (4.4K) [application/x-httpd-php]\n","Saving to: ‘iris.data’\n","\n","\riris.data             0%[                    ]       0  --.-KB/s               \riris.data           100%[===================>]   4.44K  --.-KB/s    in 0s      \n","\n","2020-05-02 19:27:17 (108 MB/s) - ‘iris.data’ saved [4551/4551]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"S89D4B9ieNE3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":191},"outputId":"80e17f61-a147-46db-c530-69fd2a8cf2e7","executionInfo":{"status":"ok","timestamp":1588447639429,"user_tz":240,"elapsed":4214,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# Take a look at the data\n","! head iris.data"],"execution_count":3,"outputs":[{"output_type":"stream","text":["5.1,3.5,1.4,0.2,Iris-setosa\n","4.9,3.0,1.4,0.2,Iris-setosa\n","4.7,3.2,1.3,0.2,Iris-setosa\n","4.6,3.1,1.5,0.2,Iris-setosa\n","5.0,3.6,1.4,0.2,Iris-setosa\n","5.4,3.9,1.7,0.4,Iris-setosa\n","4.6,3.4,1.4,0.3,Iris-setosa\n","5.0,3.4,1.5,0.2,Iris-setosa\n","4.4,2.9,1.4,0.2,Iris-setosa\n","4.9,3.1,1.5,0.1,Iris-setosa\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CFiPJ63EiGMr","colab_type":"text"},"source":["# Pre-process the data"]},{"cell_type":"code","metadata":{"id":"rABnNEH5eSTX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":206},"outputId":"c7b05b87-bace-43a8-b815-0435a912af2b","executionInfo":{"status":"ok","timestamp":1588447639431,"user_tz":240,"elapsed":4206,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# Read the data into a dataframe\n","data = pd.read_csv('iris.data', names=['f1', 'f2', 'f3', 'f4', 'label'])\n","data.head()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>f1</th>\n","      <th>f2</th>\n","      <th>f3</th>\n","      <th>f4</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    f1   f2   f3   f4        label\n","0  5.1  3.5  1.4  0.2  Iris-setosa\n","1  4.9  3.0  1.4  0.2  Iris-setosa\n","2  4.7  3.2  1.3  0.2  Iris-setosa\n","3  4.6  3.1  1.5  0.2  Iris-setosa\n","4  5.0  3.6  1.4  0.2  Iris-setosa"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"YTMzeV9cexMY","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":206},"outputId":"176ede6f-86f5-48e6-af14-4882b73e600c","executionInfo":{"status":"ok","timestamp":1588447639432,"user_tz":240,"elapsed":4197,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# One hot encode the labels\n","data = pd.concat([data, pd.get_dummies(data['label'])], axis=1)\n","del(data['label'])\n","data.head()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>f1</th>\n","      <th>f2</th>\n","      <th>f3</th>\n","      <th>f4</th>\n","      <th>Iris-setosa</th>\n","      <th>Iris-versicolor</th>\n","      <th>Iris-virginica</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    f1   f2   f3   f4  Iris-setosa  Iris-versicolor  Iris-virginica\n","0  5.1  3.5  1.4  0.2            1                0               0\n","1  4.9  3.0  1.4  0.2            1                0               0\n","2  4.7  3.2  1.3  0.2            1                0               0\n","3  4.6  3.1  1.5  0.2            1                0               0\n","4  5.0  3.6  1.4  0.2            1                0               0"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"_Vx4RX7EinN2","colab_type":"code","colab":{}},"source":["# Create training and test sets\n","train = data.sample(frac=0.8,random_state=200) \n","test = data.drop(train.index)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"o0qZaibZjQ15","colab_type":"text"},"source":["# Define the model"]},{"cell_type":"code","metadata":{"id":"FFHMlgqmfADh","colab_type":"code","colab":{}},"source":["# mynet initializes our neural network model\n","def mynet(input_size, hidden_size, num_classes):\n","\n","  # In TF 2.0+, \"Keras\" is the default API. In Keras, you assemble layers to build models.\n","  # The most common type of model is a stack of layers: the tf.keras.Sequential model.\n","  model = Sequential()\n","\n","  # Add the input and hidden layer. This all happens at once in Keras where\n","  # \"input_dim\" specifies the input layer and the hidden layer includes 8\n","  # nodes and RELU activation.\n","  model.add(Dense(hidden_size, input_dim=input_size, activation='relu'))\n","\n","  # Add the output layer with 3 nodes and softmax activation.\n","  model.add(Dense(num_classes, activation='softmax'))\n","\n","  return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cUNWTjD5nzBG","colab_type":"code","colab":{}},"source":["# Intialize the model\n","model = mynet(4, 8, 3)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"toCtuHLqgj_Y","colab_type":"code","colab":{}},"source":["# Create an SGD (stochastic gradient descent) optimizer for the training\n","sgd = optimizers.SGD(lr=0.01)\n","\n","# The model's compile step adds a few more necessary settings:\n","# 1. Loss function (how the errors will be calculated during training)\n","# 2. Optimizer (How the model parameters are updated based on the training data)\n","# 3. Metrics (Evaluation metrics used to monitor the training and testing steps)\n","model.compile(optimizer=sgd,\n","              loss='categorical_crossentropy',\n","              metrics=['accuracy'])"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SJ0fqbBdKUwJ","colab_type":"text"},"source":["# Train the model"]},{"cell_type":"code","metadata":{"id":"mVc2FoPzgpMC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"bd14b685-7d9b-4999-cca6-0db4f76f17e5","executionInfo":{"status":"ok","timestamp":1588447644924,"user_tz":240,"elapsed":9637,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# Train (aka fit) the model. An epoch is an iteration over the \n","# entire set of training data samples provided.\n","history = model.fit(train[['f1', 'f2', 'f3', 'f4']].values, \n","          train[['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']].values, \n","          epochs=400)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Epoch 1/400\n","4/4 [==============================] - 0s 2ms/step - loss: 2.5212 - accuracy: 0.3500\n","Epoch 2/400\n","4/4 [==============================] - 0s 2ms/step - loss: 1.6069 - accuracy: 0.3500\n","Epoch 3/400\n","4/4 [==============================] - 0s 2ms/step - loss: 1.1782 - accuracy: 0.3667\n","Epoch 4/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.9181 - accuracy: 0.5667\n","Epoch 5/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.8079 - accuracy: 0.7917\n","Epoch 6/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.7561 - accuracy: 0.8500\n","Epoch 7/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.7344 - accuracy: 0.8333\n","Epoch 8/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.7159 - accuracy: 0.8333\n","Epoch 9/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6954 - accuracy: 0.8583\n","Epoch 10/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6910 - accuracy: 0.8250\n","Epoch 11/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6683 - accuracy: 0.8750\n","Epoch 12/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6502 - accuracy: 0.8500\n","Epoch 13/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6404 - accuracy: 0.8667\n","Epoch 14/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6191 - accuracy: 0.8667\n","Epoch 15/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.6116 - accuracy: 0.8833\n","Epoch 16/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5969 - accuracy: 0.8667\n","Epoch 17/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5870 - accuracy: 0.8917\n","Epoch 18/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5743 - accuracy: 0.8750\n","Epoch 19/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5637 - accuracy: 0.9083\n","Epoch 20/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5562 - accuracy: 0.8917\n","Epoch 21/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.5463 - accuracy: 0.9000\n","Epoch 22/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5417 - accuracy: 0.8917\n","Epoch 23/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5279 - accuracy: 0.9083\n","Epoch 24/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5251 - accuracy: 0.9000\n","Epoch 25/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.5148 - accuracy: 0.8667\n","Epoch 26/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.5093 - accuracy: 0.9333\n","Epoch 27/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4963 - accuracy: 0.9083\n","Epoch 28/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4885 - accuracy: 0.9000\n","Epoch 29/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.4864 - accuracy: 0.9083\n","Epoch 30/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4811 - accuracy: 0.8917\n","Epoch 31/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4720 - accuracy: 0.9083\n","Epoch 32/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.4721 - accuracy: 0.9167\n","Epoch 33/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.4617 - accuracy: 0.9167\n","Epoch 34/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4513 - accuracy: 0.9333\n","Epoch 35/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4510 - accuracy: 0.8917\n","Epoch 36/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.9333\n","Epoch 37/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4458 - accuracy: 0.8750\n","Epoch 38/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4326 - accuracy: 0.9167\n","Epoch 39/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4263 - accuracy: 0.9333\n","Epoch 40/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4227 - accuracy: 0.9500\n","Epoch 41/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4158 - accuracy: 0.9250\n","Epoch 42/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4167 - accuracy: 0.9083\n","Epoch 43/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4121 - accuracy: 0.9083\n","Epoch 44/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4066 - accuracy: 0.9250\n","Epoch 45/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.4040 - accuracy: 0.9500\n","Epoch 46/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3984 - accuracy: 0.9417\n","Epoch 47/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3988 - accuracy: 0.9417\n","Epoch 48/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3935 - accuracy: 0.9583\n","Epoch 49/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3949 - accuracy: 0.9167\n","Epoch 50/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3868 - accuracy: 0.9083\n","Epoch 51/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3872 - accuracy: 0.9333\n","Epoch 52/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3773 - accuracy: 0.9417\n","Epoch 53/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3787 - accuracy: 0.9333\n","Epoch 54/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3699 - accuracy: 0.9583\n","Epoch 55/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3710 - accuracy: 0.9333\n","Epoch 56/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3564 - accuracy: 0.9500\n","Epoch 57/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3645 - accuracy: 0.9500\n","Epoch 58/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3598 - accuracy: 0.9500\n","Epoch 59/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3586 - accuracy: 0.9333\n","Epoch 60/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3525 - accuracy: 0.9417\n","Epoch 61/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3474 - accuracy: 0.9417\n","Epoch 62/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3508 - accuracy: 0.9500\n","Epoch 63/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3444 - accuracy: 0.9417\n","Epoch 64/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3479 - accuracy: 0.9500\n","Epoch 65/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3451 - accuracy: 0.9417\n","Epoch 66/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3508 - accuracy: 0.9583\n","Epoch 67/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3322 - accuracy: 0.9500\n","Epoch 68/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3343 - accuracy: 0.9583\n","Epoch 69/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3278 - accuracy: 0.9583\n","Epoch 70/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3267 - accuracy: 0.9500\n","Epoch 71/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3201 - accuracy: 0.9500\n","Epoch 72/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3265 - accuracy: 0.9583\n","Epoch 73/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3257 - accuracy: 0.9583\n","Epoch 74/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3184 - accuracy: 0.9667\n","Epoch 75/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3176 - accuracy: 0.9333\n","Epoch 76/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3171 - accuracy: 0.9500\n","Epoch 77/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3129 - accuracy: 0.9583\n","Epoch 78/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3073 - accuracy: 0.9500\n","Epoch 79/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.3120 - accuracy: 0.9500\n","Epoch 80/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3114 - accuracy: 0.9500\n","Epoch 81/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3042 - accuracy: 0.9583\n","Epoch 82/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2983 - accuracy: 0.9500\n","Epoch 83/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.3031 - accuracy: 0.9500\n","Epoch 84/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.3005 - accuracy: 0.9583\n","Epoch 85/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.2955 - accuracy: 0.9500\n","Epoch 86/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2924 - accuracy: 0.9583\n","Epoch 87/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2924 - accuracy: 0.9583\n","Epoch 88/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.3000 - accuracy: 0.9417\n","Epoch 89/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2899 - accuracy: 0.9583\n","Epoch 90/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2895 - accuracy: 0.9750\n","Epoch 91/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2822 - accuracy: 0.9500\n","Epoch 92/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2811 - accuracy: 0.9583\n","Epoch 93/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2845 - accuracy: 0.9667\n","Epoch 94/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2840 - accuracy: 0.9583\n","Epoch 95/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2819 - accuracy: 0.9583\n","Epoch 96/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2768 - accuracy: 0.9500\n","Epoch 97/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2777 - accuracy: 0.9583\n","Epoch 98/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2672 - accuracy: 0.9667\n","Epoch 99/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2782 - accuracy: 0.9417\n","Epoch 100/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2702 - accuracy: 0.9583\n","Epoch 101/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2683 - accuracy: 0.9667\n","Epoch 102/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2685 - accuracy: 0.9583\n","Epoch 103/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2622 - accuracy: 0.9667\n","Epoch 104/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2700 - accuracy: 0.9583\n","Epoch 105/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2667 - accuracy: 0.9667\n","Epoch 106/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2597 - accuracy: 0.9667\n","Epoch 107/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2611 - accuracy: 0.9667\n","Epoch 108/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2596 - accuracy: 0.9667\n","Epoch 109/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2562 - accuracy: 0.9667\n","Epoch 110/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2585 - accuracy: 0.9667\n","Epoch 111/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2554 - accuracy: 0.9667\n","Epoch 112/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2569 - accuracy: 0.9583\n","Epoch 113/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2509 - accuracy: 0.9583\n","Epoch 114/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2526 - accuracy: 0.9750\n","Epoch 115/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2623 - accuracy: 0.9500\n","Epoch 116/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2484 - accuracy: 0.9750\n","Epoch 117/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2465 - accuracy: 0.9750\n","Epoch 118/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2482 - accuracy: 0.9750\n","Epoch 119/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2422 - accuracy: 0.9750\n","Epoch 120/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2397 - accuracy: 0.9667\n","Epoch 121/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2395 - accuracy: 0.9750\n","Epoch 122/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2443 - accuracy: 0.9667\n","Epoch 123/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2409 - accuracy: 0.9667\n","Epoch 124/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2402 - accuracy: 0.9750\n","Epoch 125/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2376 - accuracy: 0.9750\n","Epoch 126/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2404 - accuracy: 0.9750\n","Epoch 127/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2350 - accuracy: 0.9750\n","Epoch 128/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2352 - accuracy: 0.9750\n","Epoch 129/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2337 - accuracy: 0.9750\n","Epoch 130/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2277 - accuracy: 0.9583\n","Epoch 131/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.2336 - accuracy: 0.9750\n","Epoch 132/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2332 - accuracy: 0.9667\n","Epoch 133/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2235 - accuracy: 0.9667\n","Epoch 134/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2318 - accuracy: 0.9667\n","Epoch 135/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2307 - accuracy: 0.9667\n","Epoch 136/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2267 - accuracy: 0.9667\n","Epoch 137/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2254 - accuracy: 0.9667\n","Epoch 138/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.2223 - accuracy: 0.9667\n","Epoch 139/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2242 - accuracy: 0.9750\n","Epoch 140/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2312 - accuracy: 0.9667\n","Epoch 141/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2235 - accuracy: 0.9667\n","Epoch 142/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.2289 - accuracy: 0.9583\n","Epoch 143/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2181 - accuracy: 0.9667\n","Epoch 144/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2170 - accuracy: 0.9667\n","Epoch 145/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2123 - accuracy: 0.9667\n","Epoch 146/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2107 - accuracy: 0.9667\n","Epoch 147/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2138 - accuracy: 0.9667\n","Epoch 148/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2144 - accuracy: 0.9667\n","Epoch 149/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2120 - accuracy: 0.9750\n","Epoch 150/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2098 - accuracy: 0.9667\n","Epoch 151/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2094 - accuracy: 0.9667\n","Epoch 152/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2078 - accuracy: 0.9667\n","Epoch 153/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2133 - accuracy: 0.9667\n","Epoch 154/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2088 - accuracy: 0.9667\n","Epoch 155/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2053 - accuracy: 0.9667\n","Epoch 156/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.2084 - accuracy: 0.9667\n","Epoch 157/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2075 - accuracy: 0.9750\n","Epoch 158/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2085 - accuracy: 0.9667\n","Epoch 159/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2011 - accuracy: 0.9667\n","Epoch 160/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2013 - accuracy: 0.9667\n","Epoch 161/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.2015 - accuracy: 0.9667\n","Epoch 162/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1975 - accuracy: 0.9667\n","Epoch 163/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1953 - accuracy: 0.9667\n","Epoch 164/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1983 - accuracy: 0.9667\n","Epoch 165/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.2046 - accuracy: 0.9750\n","Epoch 166/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1953 - accuracy: 0.9667\n","Epoch 167/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.1972 - accuracy: 0.9667\n","Epoch 168/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1903 - accuracy: 0.9667\n","Epoch 169/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1929 - accuracy: 0.9667\n","Epoch 170/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1913 - accuracy: 0.9667\n","Epoch 171/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1931 - accuracy: 0.9667\n","Epoch 172/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1908 - accuracy: 0.9667\n","Epoch 173/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1902 - accuracy: 0.9750\n","Epoch 174/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1895 - accuracy: 0.9750\n","Epoch 175/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1891 - accuracy: 0.9667\n","Epoch 176/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1922 - accuracy: 0.9667\n","Epoch 177/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1893 - accuracy: 0.9750\n","Epoch 178/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1873 - accuracy: 0.9667\n","Epoch 179/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1883 - accuracy: 0.9667\n","Epoch 180/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1854 - accuracy: 0.9667\n","Epoch 181/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1823 - accuracy: 0.9667\n","Epoch 182/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1815 - accuracy: 0.9750\n","Epoch 183/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1812 - accuracy: 0.9667\n","Epoch 184/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1837 - accuracy: 0.9750\n","Epoch 185/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1775 - accuracy: 0.9667\n","Epoch 186/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1821 - accuracy: 0.9667\n","Epoch 187/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1781 - accuracy: 0.9667\n","Epoch 188/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1759 - accuracy: 0.9750\n","Epoch 189/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1788 - accuracy: 0.9667\n","Epoch 190/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1779 - accuracy: 0.9750\n","Epoch 191/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1793 - accuracy: 0.9667\n","Epoch 192/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1831 - accuracy: 0.9667\n","Epoch 193/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1753 - accuracy: 0.9667\n","Epoch 194/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1763 - accuracy: 0.9667\n","Epoch 195/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1735 - accuracy: 0.9667\n","Epoch 196/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1788 - accuracy: 0.9667\n","Epoch 197/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1772 - accuracy: 0.9667\n","Epoch 198/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1736 - accuracy: 0.9667\n","Epoch 199/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1673 - accuracy: 0.9667\n","Epoch 200/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1798 - accuracy: 0.9750\n","Epoch 201/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1745 - accuracy: 0.9750\n","Epoch 202/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1681 - accuracy: 0.9667\n","Epoch 203/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1660 - accuracy: 0.9750\n","Epoch 204/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1715 - accuracy: 0.9750\n","Epoch 205/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1703 - accuracy: 0.9667\n","Epoch 206/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1691 - accuracy: 0.9667\n","Epoch 207/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1698 - accuracy: 0.9667\n","Epoch 208/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1678 - accuracy: 0.9667\n","Epoch 209/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1661 - accuracy: 0.9750\n","Epoch 210/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1646 - accuracy: 0.9667\n","Epoch 211/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1657 - accuracy: 0.9667\n","Epoch 212/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1655 - accuracy: 0.9750\n","Epoch 213/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1635 - accuracy: 0.9750\n","Epoch 214/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1653 - accuracy: 0.9667\n","Epoch 215/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1660 - accuracy: 0.9750\n","Epoch 216/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1603 - accuracy: 0.9667\n","Epoch 217/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1632 - accuracy: 0.9667\n","Epoch 218/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1656 - accuracy: 0.9667\n","Epoch 219/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1628 - accuracy: 0.9667\n","Epoch 220/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1603 - accuracy: 0.9750\n","Epoch 221/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1625 - accuracy: 0.9750\n","Epoch 222/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1601 - accuracy: 0.9667\n","Epoch 223/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.1611 - accuracy: 0.9667\n","Epoch 224/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1585 - accuracy: 0.9667\n","Epoch 225/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1574 - accuracy: 0.9750\n","Epoch 226/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1623 - accuracy: 0.9667\n","Epoch 227/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1557 - accuracy: 0.9667\n","Epoch 228/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1570 - accuracy: 0.9667\n","Epoch 229/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1591 - accuracy: 0.9667\n","Epoch 230/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1522 - accuracy: 0.9667\n","Epoch 231/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1614 - accuracy: 0.9750\n","Epoch 232/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1522 - accuracy: 0.9667\n","Epoch 233/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1524 - accuracy: 0.9667\n","Epoch 234/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1530 - accuracy: 0.9833\n","Epoch 235/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1567 - accuracy: 0.9750\n","Epoch 236/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1515 - accuracy: 0.9750\n","Epoch 237/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1493 - accuracy: 0.9667\n","Epoch 238/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1536 - accuracy: 0.9667\n","Epoch 239/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1509 - accuracy: 0.9750\n","Epoch 240/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1592 - accuracy: 0.9667\n","Epoch 241/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1498 - accuracy: 0.9750\n","Epoch 242/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1493 - accuracy: 0.9667\n","Epoch 243/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1556 - accuracy: 0.9750\n","Epoch 244/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1535 - accuracy: 0.9750\n","Epoch 245/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1481 - accuracy: 0.9667\n","Epoch 246/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1457 - accuracy: 0.9667\n","Epoch 247/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1489 - accuracy: 0.9750\n","Epoch 248/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1498 - accuracy: 0.9750\n","Epoch 249/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1471 - accuracy: 0.9667\n","Epoch 250/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1499 - accuracy: 0.9667\n","Epoch 251/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1513 - accuracy: 0.9750\n","Epoch 252/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1482 - accuracy: 0.9750\n","Epoch 253/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1476 - accuracy: 0.9667\n","Epoch 254/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1419 - accuracy: 0.9750\n","Epoch 255/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1442 - accuracy: 0.9667\n","Epoch 256/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1420 - accuracy: 0.9667\n","Epoch 257/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1442 - accuracy: 0.9667\n","Epoch 258/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1471 - accuracy: 0.9750\n","Epoch 259/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1442 - accuracy: 0.9667\n","Epoch 260/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1475 - accuracy: 0.9667\n","Epoch 261/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1469 - accuracy: 0.9667\n","Epoch 262/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1405 - accuracy: 0.9667\n","Epoch 263/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1408 - accuracy: 0.9667\n","Epoch 264/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1433 - accuracy: 0.9667\n","Epoch 265/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1397 - accuracy: 0.9750\n","Epoch 266/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1418 - accuracy: 0.9750\n","Epoch 267/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1407 - accuracy: 0.9667\n","Epoch 268/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.1370 - accuracy: 0.9667\n","Epoch 269/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1411 - accuracy: 0.9667\n","Epoch 270/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1404 - accuracy: 0.9750\n","Epoch 271/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1407 - accuracy: 0.9750\n","Epoch 272/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1362 - accuracy: 0.9667\n","Epoch 273/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1344 - accuracy: 0.9750\n","Epoch 274/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1391 - accuracy: 0.9667\n","Epoch 275/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1367 - accuracy: 0.9750\n","Epoch 276/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1345 - accuracy: 0.9750\n","Epoch 277/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1361 - accuracy: 0.9667\n","Epoch 278/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1333 - accuracy: 0.9667\n","Epoch 279/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1347 - accuracy: 0.9667\n","Epoch 280/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1323 - accuracy: 0.9750\n","Epoch 281/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1351 - accuracy: 0.9750\n","Epoch 282/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1419 - accuracy: 0.9667\n","Epoch 283/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1403 - accuracy: 0.9750\n","Epoch 284/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1315 - accuracy: 0.9750\n","Epoch 285/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1344 - accuracy: 0.9750\n","Epoch 286/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1342 - accuracy: 0.9667\n","Epoch 287/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1333 - accuracy: 0.9667\n","Epoch 288/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1327 - accuracy: 0.9750\n","Epoch 289/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1297 - accuracy: 0.9750\n","Epoch 290/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1345 - accuracy: 0.9750\n","Epoch 291/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1321 - accuracy: 0.9667\n","Epoch 292/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1341 - accuracy: 0.9750\n","Epoch 293/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1312 - accuracy: 0.9750\n","Epoch 294/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1307 - accuracy: 0.9750\n","Epoch 295/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1321 - accuracy: 0.9667\n","Epoch 296/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1358 - accuracy: 0.9667\n","Epoch 297/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1332 - accuracy: 0.9667\n","Epoch 298/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1288 - accuracy: 0.9667\n","Epoch 299/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1340 - accuracy: 0.9750\n","Epoch 300/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1313 - accuracy: 0.9750\n","Epoch 301/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1274 - accuracy: 0.9750\n","Epoch 302/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1314 - accuracy: 0.9667\n","Epoch 303/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1247 - accuracy: 0.9750\n","Epoch 304/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1260 - accuracy: 0.9750\n","Epoch 305/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1296 - accuracy: 0.9750\n","Epoch 306/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1250 - accuracy: 0.9750\n","Epoch 307/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1239 - accuracy: 0.9667\n","Epoch 308/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1307 - accuracy: 0.9750\n","Epoch 309/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1260 - accuracy: 0.9750\n","Epoch 310/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1258 - accuracy: 0.9667\n","Epoch 311/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1309 - accuracy: 0.9750\n","Epoch 312/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1335 - accuracy: 0.9750\n","Epoch 313/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1224 - accuracy: 0.9750\n","Epoch 314/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1227 - accuracy: 0.9750\n","Epoch 315/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1303 - accuracy: 0.9833\n","Epoch 316/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1284 - accuracy: 0.9750\n","Epoch 317/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1230 - accuracy: 0.9833\n","Epoch 318/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1217 - accuracy: 0.9750\n","Epoch 319/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1255 - accuracy: 0.9750\n","Epoch 320/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1274 - accuracy: 0.9750\n","Epoch 321/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1261 - accuracy: 0.9833\n","Epoch 322/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1197 - accuracy: 0.9750\n","Epoch 323/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.1254 - accuracy: 0.9750\n","Epoch 324/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1230 - accuracy: 0.9750\n","Epoch 325/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1239 - accuracy: 0.9750\n","Epoch 326/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1244 - accuracy: 0.9750\n","Epoch 327/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1250 - accuracy: 0.9750\n","Epoch 328/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1246 - accuracy: 0.9750\n","Epoch 329/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1241 - accuracy: 0.9833\n","Epoch 330/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1214 - accuracy: 0.9750\n","Epoch 331/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1221 - accuracy: 0.9667\n","Epoch 332/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1260 - accuracy: 0.9667\n","Epoch 333/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1225 - accuracy: 0.9750\n","Epoch 334/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1225 - accuracy: 0.9750\n","Epoch 335/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1171 - accuracy: 0.9750\n","Epoch 336/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9750\n","Epoch 337/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1196 - accuracy: 0.9750\n","Epoch 338/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9750\n","Epoch 339/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1204 - accuracy: 0.9750\n","Epoch 340/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1161 - accuracy: 0.9750\n","Epoch 341/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1155 - accuracy: 0.9833\n","Epoch 342/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1200 - accuracy: 0.9750\n","Epoch 343/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1177 - accuracy: 0.9667\n","Epoch 344/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.1184 - accuracy: 0.9750\n","Epoch 345/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1172 - accuracy: 0.9750\n","Epoch 346/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1194 - accuracy: 0.9750\n","Epoch 347/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1154 - accuracy: 0.9833\n","Epoch 348/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1187 - accuracy: 0.9750\n","Epoch 349/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1166 - accuracy: 0.9750\n","Epoch 350/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1179 - accuracy: 0.9750\n","Epoch 351/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1126 - accuracy: 0.9750\n","Epoch 352/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1173 - accuracy: 0.9667\n","Epoch 353/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1148 - accuracy: 0.9750\n","Epoch 354/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1141 - accuracy: 0.9833\n","Epoch 355/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1155 - accuracy: 0.9750\n","Epoch 356/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1131 - accuracy: 0.9750\n","Epoch 357/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9667\n","Epoch 358/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1170 - accuracy: 0.9833\n","Epoch 359/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1129 - accuracy: 0.9750\n","Epoch 360/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1142 - accuracy: 0.9833\n","Epoch 361/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1145 - accuracy: 0.9833\n","Epoch 362/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1149 - accuracy: 0.9750\n","Epoch 363/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1131 - accuracy: 0.9750\n","Epoch 364/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1142 - accuracy: 0.9750\n","Epoch 365/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1158 - accuracy: 0.9750\n","Epoch 366/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1184 - accuracy: 0.9750\n","Epoch 367/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1098 - accuracy: 0.9833\n","Epoch 368/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1102 - accuracy: 0.9833\n","Epoch 369/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1120 - accuracy: 0.9750\n","Epoch 370/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1170 - accuracy: 0.9750\n","Epoch 371/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1126 - accuracy: 0.9750\n","Epoch 372/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1172 - accuracy: 0.9750\n","Epoch 373/400\n","4/4 [==============================] - 0s 3ms/step - loss: 0.1148 - accuracy: 0.9750\n","Epoch 374/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1058 - accuracy: 0.9750\n","Epoch 375/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1107 - accuracy: 0.9750\n","Epoch 376/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1057 - accuracy: 0.9750\n","Epoch 377/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1089 - accuracy: 0.9833\n","Epoch 378/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1084 - accuracy: 0.9833\n","Epoch 379/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1147 - accuracy: 0.9750\n","Epoch 380/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1075 - accuracy: 0.9750\n","Epoch 381/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1117 - accuracy: 0.9833\n","Epoch 382/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1110 - accuracy: 0.9750\n","Epoch 383/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1176 - accuracy: 0.9750\n","Epoch 384/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1166 - accuracy: 0.9750\n","Epoch 385/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1088 - accuracy: 0.9833\n","Epoch 386/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1070 - accuracy: 0.9750\n","Epoch 387/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1070 - accuracy: 0.9750\n","Epoch 388/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1087 - accuracy: 0.9750\n","Epoch 389/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1101 - accuracy: 0.9833\n","Epoch 390/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1073 - accuracy: 0.9750\n","Epoch 391/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1063 - accuracy: 0.9750\n","Epoch 392/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1102 - accuracy: 0.9750\n","Epoch 393/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1143 - accuracy: 0.9667\n","Epoch 394/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1129 - accuracy: 0.9750\n","Epoch 395/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1081 - accuracy: 0.9833\n","Epoch 396/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1115 - accuracy: 0.9750\n","Epoch 397/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1068 - accuracy: 0.9750\n","Epoch 398/400\n","4/4 [==============================] - 0s 1ms/step - loss: 0.1077 - accuracy: 0.9833\n","Epoch 399/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1057 - accuracy: 0.9750\n","Epoch 400/400\n","4/4 [==============================] - 0s 2ms/step - loss: 0.1071 - accuracy: 0.9667\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vsIZbHxagxJy","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":312},"outputId":"8b02d035-102d-4ff2-b5ad-c94ccac6f406","executionInfo":{"status":"ok","timestamp":1588450571033,"user_tz":240,"elapsed":583,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# Plot the training loss for each epoch to see how the model converged.\n","history_dict = history.history\n","plt.plot(range(400), history_dict['loss'], 'bo--', label='Training loss')\n","plt.title('Training loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fd6da629978>"]},"metadata":{"tags":[]},"execution_count":14},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"0L3p4SUwKYLt","colab_type":"text"},"source":["# Evaluate the model"]},{"cell_type":"code","metadata":{"id":"k1Hhm99UGh1F","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":69},"outputId":"c8e42b58-9bdd-4a81-d06a-bccf1956895f","executionInfo":{"status":"ok","timestamp":1588447804157,"user_tz":240,"elapsed":498,"user":{"displayName":"Daniel Whitenack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjAksu_ElVnuka7KuOLHjyaXiy4v9BA2P1AqagM2w=s64","userId":"15195746376658990804"}}},"source":["# Evaluate the model on the test set. \n","test_loss, test_acc = model.evaluate(test[['f1', 'f2', 'f3', 'f4']].values, \n","          test[['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']].values, \n","          verbose=2)\n","\n","print('\\nTest accuracy:', test_acc)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["1/1 - 0s - loss: 0.1632 - accuracy: 0.9333\n","\n","Test accuracy: 0.9333333373069763\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fYGfykR5oFZK","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}