bayesian-hyperparameter-tuning.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\npd.set_option('display.max_colwidth',0)\nfrom keras.utils import np_utils\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\nfrom keras.utils import np_utils\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom matplotlib import pyplot as plt\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.optimizers import Adam, RMSprop,Adamax\nfrom sklearn.model_selection import GridSearchCV\nfrom keras.wrappers.scikit_learn import KerasClassifier\n# Function to create model, required for KerasClassifier","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# fix random seed for reproducibility\nseed = 7\nnp.random.seed(seed)","execution_count":2,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# Loading the training set\ntrain_ds = np.load('/kaggle/input/ift3395-6390-quickdraw/train.npz')\ntrain_x = train_ds['arr_0']\ntrain_y = train_ds['arr_1']\n\ntrain_x.shape, train_y.shape","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"((1500, 784), (1500,))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Loading the test data\ntest_ds = np.load('/kaggle/input/ift3395-6390-quickdraw/test.npz')\ntest_x = test_ds['arr_0']\n\ntest_x.shape","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"(60000, 784)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Normalizing the values, so that the algorithm will converge faster\ntrain_x=train_x/255.0","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# doing one hot encoding to the output \nn_classes = 6\ntrain_y = np_utils.to_categorical(train_y, n_classes)\nprint(\"After one-hot encoding: \", train_y.shape)","execution_count":6,"outputs":[{"output_type":"stream","text":"After one-hot encoding:  (1500, 6)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Reshaping it as a image\ntrain_x = train_x.reshape(train_x.shape[0], 28, 28, 1)\n","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nX_train,X_validation,y_train,y_validation=train_test_split(train_x, train_y , test_size=0.2)","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# you can pass the parameters here and check how the images are\ndatagen = ImageDataGenerator(fill_mode='nearest')\n\n# fit parameters from data\ndatagen.fit(X_train)\n\nfig, ax = plt.subplots(nrows=1, ncols=5, figsize=(28,28))\n\n# plt.imshow(train_x[111].reshape(28, 28))\n# plt.show()\n# Configure batch size and retrieve one batch of images\nfor X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):\n    # Show 9 images\n    for i in range(0, 5):\n        \n        ax[i].imshow(X_batch[i].reshape(28,28))\n        ax[i].axis('off')\n    break","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2016x2016 with 5 Axes>","image/png":"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= ImageDataGenerator(rotation_range=45, width_shift_range=.2, \n                             height_shift_range=.2,vertical_flip=True,\n                             fill_mode='nearest',horizontal_flip=True,zoom_range=0.2,shear_range = 0.2 )\n\n# fit parameters from data\ndatagen.fit(X_train)","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndef get_model(opt,activation,dropout,lr):\n    model = Sequential()\n    model.add(Conv2D(64, kernel_size=(3,3), strides=(1,1), padding='same', activation=activation, input_shape=(28,28,1)))\n    model.add(MaxPool2D(pool_size=(2,2) , strides=(2, 2)))\n\n    model.add(Conv2D(128, kernel_size=(3,3), strides=(1,1), padding='same', activation=activation, ))\n    model.add(MaxPool2D(pool_size=(1,1)))\n\n    model.add(Conv2D(256, kernel_size=(3,3), strides=(1,1), padding='same', activation=activation, ))\n    model.add(MaxPool2D(pool_size=(1,1)))\n    \n#     model.add(Conv2D(512, kernel_size=(3,3), strides=(1,1), padding='same', activation=activation, ))\n#     model.add(MaxPool2D(pool_size=(1,1)))\n\n    # flatten output of conv\n    model.add(Flatten())\n    # hidden layer\n    model.add(Dense(512, activation=activation))\n    model.add(Dropout(dropout))\n\n    model.add(Dense(256, activation=activation))\n    model.add(Dropout(dropout))\n\n    model.add(Dense(6, activation='softmax'))\n    # compiling the sequential model\n    if opt == 'adam':\n        optimizer = Adam(lr=lr)\n    elif opt == 'adamax':\n        optimizer = Adamax(lr=lr)\n    elif opt == 'rmsprop':\n        optimizer = RMSprop(lr=lr)\n    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=opt)\n    return model","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def train_evaluate(parameterization):\n    acc = 0\n    batch_size=32\n    mymodel = get_model(opt=parameterization[\"opt\"],activation=parameterization[\"activation\"], dropout=parameterization[\"dropout\"], lr=parameterization[\"lr\"])\n    mymodel.fit_generator(datagen.flow(X_train, y_train), epochs=100, verbose=0)\n    acc = mymodel.evaluate(X_validation, y_validation)[1]\n    print(parameterization, acc)\n    del mymodel\n    return acc","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip3 install ax-platform","execution_count":13,"outputs":[{"output_type":"stream","text":"Collecting ax-platform\n  Downloading ax_platform-0.1.18-py3-none-any.whl (675 kB)\n\u001b[K     |████████████████████████████████| 675 kB 1.2 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: typeguard in /opt/conda/lib/python3.7/site-packages (from ax-platform) (2.10.0)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from ax-platform) (0.23.2)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from ax-platform) (1.1.4)\nCollecting botorch>=0.3.2\n  Downloading botorch-0.3.2-py3-none-any.whl (327 kB)\n\u001b[K     |████████████████████████████████| 327 kB 5.5 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: jinja2 in /opt/conda/lib/python3.7/site-packages (from ax-platform) (2.11.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from ax-platform) (1.4.1)\nRequirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from ax-platform) (4.12.0)\nRequirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->ax-platform) (1.18.5)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->ax-platform) (2.1.0)\nRequirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->ax-platform) (0.14.1)\nRequirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas->ax-platform) (2019.3)\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->ax-platform) (2.8.1)\nCollecting gpytorch>=1.2\n  Downloading gpytorch-1.2.1.tar.gz (288 kB)\n\u001b[K     |████████████████████████████████| 288 kB 6.4 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: torch>=1.6 in /opt/conda/lib/python3.7/site-packages (from botorch>=0.3.2->ax-platform) (1.6.0)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/lib/python3.7/site-packages (from jinja2->ax-platform) (1.1.1)\nRequirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.7/site-packages (from plotly->ax-platform) (1.3.3)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from plotly->ax-platform) (1.14.0)\nRequirement already satisfied: future in /opt/conda/lib/python3.7/site-packages (from torch>=1.6->botorch>=0.3.2->ax-platform) (0.18.2)\nBuilding wheels for collected packages: gpytorch\n  Building wheel for gpytorch (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for gpytorch: filename=gpytorch-1.2.1-py2.py3-none-any.whl size=463973 sha256=2884514c7810dea342702c7aa65a85a5c39d4c3db1eaa9318b850ce7ba2e0421\n  Stored in directory: /root/.cache/pip/wheels/5f/2f/85/fa26b1b3b26cb1010a00841a49d387a3f8b8dacb11d1301826\nSuccessfully built gpytorch\nInstalling collected packages: gpytorch, botorch, ax-platform\nSuccessfully installed ax-platform-0.1.18 botorch-0.3.2 gpytorch-1.2.1\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from ax.service.managed_loop import optimize\nbest_parameters, values, experiment, model = optimize(\n     parameters=[\n                 {\"name\": \"opt\", \"type\": \"choice\", \"values\": ['adam', 'adamax','rmsprop']},\n                 {\"name\": \"activation\", \"type\": \"choice\", \"values\": ['relu', 'tanh']},\n                 {\"name\": \"dropout\", \"type\": \"choice\", \"values\": [0.0, 0.25, 0.50, 0.75, 0.99]},\n#                  {\"name\": \"batch_size\", \"type\": \"choice\", \"values\": [10, 20, 40, 60, 80, 100] },\n#                  {\"name\": \"lr\", \"type\": \"range\", \"bounds\": [1e-6, 0.4], \"log_scale\": True}\n                 {\"name\": \"lr\", \"type\": \"choice\", \"values\": [0.003, 0.03, 0.1, 0.2, 0.3,0.0003] }\n                ],\n    evaluation_function=train_evaluate,\n    objective_name=\"acc\",\n     # total trails. Based on the dateset size you can change this value. This value is used to determine the number of exploration and exploitation steps. \n    total_trials=5,\n    )","execution_count":15,"outputs":[{"output_type":"stream","text":"[INFO 11-22 09:29:22] ax.service.utils.instantiation: Inferred value type of ParameterType.STRING for parameter opt. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n[INFO 11-22 09:29:22] ax.service.utils.instantiation: Inferred value type of ParameterType.STRING for parameter activation. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n[INFO 11-22 09:29:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter dropout. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n[INFO 11-22 09:29:22] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter lr. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n[INFO 11-22 09:29:22] ax.modelbridge.dispatch_utils: Using Sobol generation strategy.\n[INFO 11-22 09:29:22] ax.service.managed_loop: Started full optimization with 5 steps.\n[INFO 11-22 09:29:22] ax.service.managed_loop: Running optimization trial 1...\n","name":"stderr"},{"output_type":"stream","text":"10/10 [==============================] - 0s 5ms/step - loss: 1.4564 - accuracy: 0.4367\n","name":"stdout"},{"output_type":"stream","text":"[INFO 11-22 09:30:12] ax.service.managed_loop: Running optimization trial 2...\n","name":"stderr"},{"output_type":"stream","text":"{'opt': 'adam', 'activation': 'tanh', 'dropout': 0.5, 'lr': 0.003} 0.43666666746139526\n10/10 [==============================] - 0s 4ms/step - loss: 1.0972 - accuracy: 0.6267\n","name":"stdout"},{"output_type":"stream","text":"[INFO 11-22 09:31:01] ax.service.managed_loop: Running optimization trial 3...\n","name":"stderr"},{"output_type":"stream","text":"{'opt': 'adamax', 'activation': 'relu', 'dropout': 0.75, 'lr': 0.003} 0.6266666650772095\n10/10 [==============================] - 0s 3ms/step - loss: 1.7946 - accuracy: 0.1467\n","name":"stdout"},{"output_type":"stream","text":"[INFO 11-22 09:31:51] ax.service.managed_loop: Running optimization trial 4...\n","name":"stderr"},{"output_type":"stream","text":"{'opt': 'adamax', 'activation': 'relu', 'dropout': 0.99, 'lr': 0.1} 0.14666666090488434\n10/10 [==============================] - 0s 3ms/step - loss: 1.8108 - accuracy: 0.1933\n","name":"stdout"},{"output_type":"stream","text":"[INFO 11-22 09:32:43] ax.service.managed_loop: Running optimization trial 5...\n","name":"stderr"},{"output_type":"stream","text":"{'opt': 'rmsprop', 'activation': 'tanh', 'dropout': 0.25, 'lr': 0.0003} 0.19333332777023315\n10/10 [==============================] - 0s 3ms/step - loss: 1.7965 - accuracy: 0.1467\n{'opt': 'adam', 'activation': 'tanh', 'dropout': 0.99, 'lr': 0.0003} 0.14666666090488434\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Get the best hyper parameters\ndata = experiment.fetch_data()\ndf = data.df\nbest_arm_name = df.arm_name[df[\"mean\"] == df[\"mean\"].max()].values[0]\nbest_arm = experiment.arms_by_name[best_arm_name]\n\nprint(best_parameters)\nprint(best_arm)","execution_count":16,"outputs":[{"output_type":"stream","text":"{'opt': 'adamax', 'activation': 'relu', 'dropout': 0.75, 'lr': 0.003}\nArm(name='1_0', parameters={'opt': 'adamax', 'activation': 'relu', 'dropout': 0.75, 'lr': 0.003})\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#To evaluate on the best parameter\ntrain_evaluate(best_parameters)","execution_count":18,"outputs":[{"output_type":"stream","text":"10/10 [==============================] - 0s 3ms/step - loss: 1.1534 - accuracy: 0.6167\n{'opt': 'adamax', 'activation': 'relu', 'dropout': 0.75, 'lr': 0.003} 0.6166666746139526\n","name":"stdout"},{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"0.6166666746139526"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# opt,activation,dropout,lr\n#('adamax','relu', 0.75,0.01)  out of 30 trails ~70% val accuracy\n\ntest_model=get_model('adamax','relu', 0.75,0.01)","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# from tensorflow.keras.applications.vgg16 import VGG16\n\n# # base_model = VGG16(input_shape = (224, 224, 3), # Shape of our images\n# # include_top = False, # Leave out the last fully connected layer\n# # weights = 'imagenet')\n\n\n\n# # model.add(base_model)\n\n\n# model = Sequential()\n\n# # model.add(base_model)\n\n# model.add(Conv2D(32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', input_shape=(28,28,1)))\n\n# model.add(MaxPool2D(pool_size=(2,2) , strides=(2, 2)))\n\n# model.add(Conv2D(64, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', ))\n# model.add(MaxPool2D(pool_size=(1,1)))\n\n# model.add(Conv2D(128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', ))\n# model.add(MaxPool2D(pool_size=(1,1)))\n\n# model.add(Conv2D(256, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', ))\n# model.add(MaxPool2D(pool_size=(1,1)))\n\n# model.add(Conv2D(512, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', ))\n# model.add(MaxPool2D(pool_size=(1,1)))\n\n\n\n# # flatten output of conv\n# model.add(Flatten())\n# # hidden layer\n# model.add(Dense(512, activation='relu'))\n# model.add(Dropout(0.25))\n\n# model.add(Dense(256, activation='relu'))\n# model.add(Dropout(0.25))\n\n\n\n# # output layer\n# model.add(Dense(6, activation='softmax'))\n# # compiling the sequential model\n# model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nbatch_size = 32\n# Rotate images by 90 degrees\n\ndatagen = ImageDataGenerator(rotation_range=30, width_shift_range=.2, \n                             height_shift_range=.2,vertical_flip=True,\n                             fill_mode='nearest',horizontal_flip=True,zoom_range=0.1,shear_range = 0.2 )\n\n# fit parameters from data\ndatagen.fit(X_train)\n\n# training the model for 10 epochs\nhistory = test_model.fit_generator(datagen.flow(X_train, y_train, batch_size=batch_size),\n#                         steps_per_epoch=X_train.shape[0] // batch_size,\n                        epochs=150,\n                        validation_data=(X_validation, y_validation)#,callbacks=[early_stopping] \n                             )\n# model.fit(X_train, y_train, batch_size=128, epochs=10, validation_data=(X_validation, y_validation))","execution_count":20,"outputs":[{"output_type":"stream","text":"Epoch 1/150\n38/38 [==============================] - 1s 19ms/step - loss: 1.8160 - accuracy: 0.1492 - val_loss: 1.7912 - val_accuracy: 0.1467\nEpoch 2/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.7927 - accuracy: 0.1642 - val_loss: 1.7911 - val_accuracy: 0.2033\nEpoch 3/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.7892 - accuracy: 0.1892 - val_loss: 1.7860 - val_accuracy: 0.1700\nEpoch 4/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.7919 - accuracy: 0.1708 - val_loss: 1.7868 - val_accuracy: 0.2400\nEpoch 5/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.7900 - accuracy: 0.1842 - val_loss: 1.7821 - val_accuracy: 0.2700\nEpoch 6/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.7879 - accuracy: 0.1883 - val_loss: 1.7679 - val_accuracy: 0.3400\nEpoch 7/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.7785 - accuracy: 0.2025 - val_loss: 1.7485 - val_accuracy: 0.3300\nEpoch 8/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.7675 - accuracy: 0.2333 - val_loss: 1.7208 - val_accuracy: 0.3167\nEpoch 9/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.7528 - accuracy: 0.2200 - val_loss: 1.6849 - val_accuracy: 0.3000\nEpoch 10/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.7337 - accuracy: 0.2500 - val_loss: 1.6348 - val_accuracy: 0.3000\nEpoch 11/150\n38/38 [==============================] - 1s 18ms/step - loss: 1.7055 - accuracy: 0.2500 - val_loss: 1.6104 - val_accuracy: 0.3167\nEpoch 12/150\n38/38 [==============================] - 1s 19ms/step - loss: 1.7100 - accuracy: 0.2608 - val_loss: 1.6059 - val_accuracy: 0.2833\nEpoch 13/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.7119 - accuracy: 0.2517 - val_loss: 1.5951 - val_accuracy: 0.3033\nEpoch 14/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.7043 - accuracy: 0.2500 - val_loss: 1.5752 - val_accuracy: 0.3467\nEpoch 15/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.7051 - accuracy: 0.2583 - val_loss: 1.5640 - val_accuracy: 0.3200\nEpoch 16/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6966 - accuracy: 0.2658 - val_loss: 1.5664 - val_accuracy: 0.3033\nEpoch 17/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6810 - accuracy: 0.2642 - val_loss: 1.5535 - val_accuracy: 0.3100\nEpoch 18/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6619 - accuracy: 0.2858 - val_loss: 1.5440 - val_accuracy: 0.3600\nEpoch 19/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6635 - accuracy: 0.2767 - val_loss: 1.5349 - val_accuracy: 0.3200\nEpoch 20/150\n38/38 [==============================] - 1s 18ms/step - loss: 1.6469 - accuracy: 0.2833 - val_loss: 1.5367 - val_accuracy: 0.4100\nEpoch 21/150\n38/38 [==============================] - 1s 23ms/step - loss: 1.6561 - accuracy: 0.2958 - val_loss: 1.5412 - val_accuracy: 0.4433\nEpoch 22/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6492 - accuracy: 0.3000 - val_loss: 1.5343 - val_accuracy: 0.4167\nEpoch 23/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6420 - accuracy: 0.3058 - val_loss: 1.5157 - val_accuracy: 0.3933\nEpoch 24/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6539 - accuracy: 0.3017 - val_loss: 1.5431 - val_accuracy: 0.3967\nEpoch 25/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6510 - accuracy: 0.2992 - val_loss: 1.5233 - val_accuracy: 0.4133\nEpoch 26/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6424 - accuracy: 0.2908 - val_loss: 1.5556 - val_accuracy: 0.3833\nEpoch 27/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6297 - accuracy: 0.3067 - val_loss: 1.5107 - val_accuracy: 0.4367\nEpoch 28/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6325 - accuracy: 0.3042 - val_loss: 1.4863 - val_accuracy: 0.4400\nEpoch 29/150\n38/38 [==============================] - 1s 22ms/step - loss: 1.6279 - accuracy: 0.3242 - val_loss: 1.4767 - val_accuracy: 0.4500\nEpoch 30/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6368 - accuracy: 0.3017 - val_loss: 1.4623 - val_accuracy: 0.4467\nEpoch 31/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6204 - accuracy: 0.3175 - val_loss: 1.4570 - val_accuracy: 0.4400\nEpoch 32/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.6281 - accuracy: 0.3000 - val_loss: 1.5156 - val_accuracy: 0.4000\nEpoch 33/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6013 - accuracy: 0.3183 - val_loss: 1.4423 - val_accuracy: 0.4200\nEpoch 34/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.6304 - accuracy: 0.3183 - val_loss: 1.4814 - val_accuracy: 0.3867\nEpoch 35/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.5961 - accuracy: 0.3450 - val_loss: 1.4277 - val_accuracy: 0.4567\nEpoch 36/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.5946 - accuracy: 0.3250 - val_loss: 1.4567 - val_accuracy: 0.3733\nEpoch 37/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.5573 - accuracy: 0.3550 - val_loss: 1.4707 - val_accuracy: 0.3533\nEpoch 38/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.5560 - accuracy: 0.3450 - val_loss: 1.3577 - val_accuracy: 0.4600\nEpoch 39/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.5513 - accuracy: 0.3700 - val_loss: 1.3849 - val_accuracy: 0.4133\nEpoch 40/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.5641 - accuracy: 0.3658 - val_loss: 1.4041 - val_accuracy: 0.3967\nEpoch 41/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.5558 - accuracy: 0.3433 - val_loss: 1.4527 - val_accuracy: 0.3867\nEpoch 42/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.5485 - accuracy: 0.3592 - val_loss: 1.3619 - val_accuracy: 0.4500\nEpoch 43/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.5452 - accuracy: 0.3608 - val_loss: 1.3457 - val_accuracy: 0.4567\nEpoch 44/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.5187 - accuracy: 0.3625 - val_loss: 1.5129 - val_accuracy: 0.3800\nEpoch 45/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.5227 - accuracy: 0.3908 - val_loss: 1.3610 - val_accuracy: 0.4633\nEpoch 46/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.5183 - accuracy: 0.3667 - val_loss: 1.2831 - val_accuracy: 0.5233\nEpoch 47/150\n38/38 [==============================] - 1s 20ms/step - loss: 1.5133 - accuracy: 0.3883 - val_loss: 1.2850 - val_accuracy: 0.5133\nEpoch 48/150\n38/38 [==============================] - 1s 22ms/step - loss: 1.5123 - accuracy: 0.3833 - val_loss: 1.2464 - val_accuracy: 0.5300\nEpoch 49/150\n38/38 [==============================] - 1s 19ms/step - loss: 1.4830 - accuracy: 0.3983 - val_loss: 1.4012 - val_accuracy: 0.4500\nEpoch 50/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.4761 - accuracy: 0.3967 - val_loss: 1.3954 - val_accuracy: 0.4833\nEpoch 51/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.4716 - accuracy: 0.4042 - val_loss: 1.4969 - val_accuracy: 0.4400\nEpoch 52/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.4809 - accuracy: 0.4033 - val_loss: 1.2369 - val_accuracy: 0.5433\nEpoch 53/150\n38/38 [==============================] - 1s 19ms/step - loss: 1.4994 - accuracy: 0.3825 - val_loss: 1.3484 - val_accuracy: 0.4500\nEpoch 54/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.4466 - accuracy: 0.4133 - val_loss: 1.2849 - val_accuracy: 0.4900\nEpoch 55/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.4623 - accuracy: 0.4250 - val_loss: 1.1998 - val_accuracy: 0.5567\nEpoch 56/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.4627 - accuracy: 0.4050 - val_loss: 1.2600 - val_accuracy: 0.5067\nEpoch 57/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.4546 - accuracy: 0.4283 - val_loss: 1.3135 - val_accuracy: 0.5133\nEpoch 58/150\n","name":"stdout"},{"output_type":"stream","text":"38/38 [==============================] - 1s 14ms/step - loss: 1.4559 - accuracy: 0.4283 - val_loss: 1.3436 - val_accuracy: 0.5000\nEpoch 59/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.4195 - accuracy: 0.4292 - val_loss: 1.2370 - val_accuracy: 0.5233\nEpoch 60/150\n38/38 [==============================] - 1s 18ms/step - loss: 1.4444 - accuracy: 0.4433 - val_loss: 1.2464 - val_accuracy: 0.5200\nEpoch 61/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.4138 - accuracy: 0.4383 - val_loss: 1.1541 - val_accuracy: 0.5533\nEpoch 62/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3835 - accuracy: 0.4642 - val_loss: 1.1079 - val_accuracy: 0.5933\nEpoch 63/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3903 - accuracy: 0.4492 - val_loss: 1.0678 - val_accuracy: 0.6633\nEpoch 64/150\n38/38 [==============================] - 1s 22ms/step - loss: 1.3499 - accuracy: 0.4533 - val_loss: 1.1849 - val_accuracy: 0.5533\nEpoch 65/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3721 - accuracy: 0.4867 - val_loss: 1.1385 - val_accuracy: 0.5600\nEpoch 66/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3595 - accuracy: 0.4692 - val_loss: 1.1035 - val_accuracy: 0.5667\nEpoch 67/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3344 - accuracy: 0.4908 - val_loss: 1.0662 - val_accuracy: 0.6100\nEpoch 68/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3494 - accuracy: 0.4892 - val_loss: 1.0977 - val_accuracy: 0.5733\nEpoch 69/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2931 - accuracy: 0.4767 - val_loss: 1.0200 - val_accuracy: 0.6333\nEpoch 70/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.3000 - accuracy: 0.4925 - val_loss: 1.0783 - val_accuracy: 0.5967\nEpoch 71/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.3092 - accuracy: 0.4925 - val_loss: 1.2510 - val_accuracy: 0.5267\nEpoch 72/150\n38/38 [==============================] - 1s 20ms/step - loss: 1.2828 - accuracy: 0.5067 - val_loss: 1.1142 - val_accuracy: 0.6067\nEpoch 73/150\n38/38 [==============================] - 1s 22ms/step - loss: 1.2353 - accuracy: 0.5417 - val_loss: 1.1303 - val_accuracy: 0.5433\nEpoch 74/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2651 - accuracy: 0.5108 - val_loss: 0.9875 - val_accuracy: 0.6600\nEpoch 75/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2768 - accuracy: 0.5225 - val_loss: 1.0095 - val_accuracy: 0.6400\nEpoch 76/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.2651 - accuracy: 0.5208 - val_loss: 1.1426 - val_accuracy: 0.5900\nEpoch 77/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2511 - accuracy: 0.5158 - val_loss: 1.0677 - val_accuracy: 0.6200\nEpoch 78/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2432 - accuracy: 0.5325 - val_loss: 1.0537 - val_accuracy: 0.6433\nEpoch 79/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2246 - accuracy: 0.5342 - val_loss: 1.1235 - val_accuracy: 0.6100\nEpoch 80/150\n38/38 [==============================] - 1s 20ms/step - loss: 1.1996 - accuracy: 0.5567 - val_loss: 1.1455 - val_accuracy: 0.6233\nEpoch 81/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.1871 - accuracy: 0.5600 - val_loss: 1.0040 - val_accuracy: 0.6700\nEpoch 82/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2155 - accuracy: 0.5358 - val_loss: 0.9932 - val_accuracy: 0.6433\nEpoch 83/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.2057 - accuracy: 0.5517 - val_loss: 1.1016 - val_accuracy: 0.6133\nEpoch 84/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1896 - accuracy: 0.5675 - val_loss: 1.0285 - val_accuracy: 0.6600\nEpoch 85/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.1996 - accuracy: 0.5425 - val_loss: 0.9789 - val_accuracy: 0.6433\nEpoch 86/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.1496 - accuracy: 0.5725 - val_loss: 1.0182 - val_accuracy: 0.6500\nEpoch 87/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1669 - accuracy: 0.5783 - val_loss: 0.9843 - val_accuracy: 0.6567\nEpoch 88/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1612 - accuracy: 0.5600 - val_loss: 0.9978 - val_accuracy: 0.6667\nEpoch 89/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1284 - accuracy: 0.5658 - val_loss: 1.1784 - val_accuracy: 0.6300\nEpoch 90/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.2196 - accuracy: 0.5508 - val_loss: 0.9974 - val_accuracy: 0.6833\nEpoch 91/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1878 - accuracy: 0.5642 - val_loss: 0.9820 - val_accuracy: 0.6633\nEpoch 92/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1385 - accuracy: 0.5842 - val_loss: 1.1379 - val_accuracy: 0.6433\nEpoch 93/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.1170 - accuracy: 0.5783 - val_loss: 1.0206 - val_accuracy: 0.6900\nEpoch 94/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1340 - accuracy: 0.5917 - val_loss: 0.9830 - val_accuracy: 0.6900\nEpoch 95/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.1352 - accuracy: 0.5675 - val_loss: 1.0090 - val_accuracy: 0.6733\nEpoch 96/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0915 - accuracy: 0.6092 - val_loss: 0.9948 - val_accuracy: 0.6667\nEpoch 97/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.1213 - accuracy: 0.5992 - val_loss: 1.0497 - val_accuracy: 0.6633\nEpoch 98/150\n38/38 [==============================] - 1s 20ms/step - loss: 1.1091 - accuracy: 0.5842 - val_loss: 0.9279 - val_accuracy: 0.7000\nEpoch 99/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.1237 - accuracy: 0.5758 - val_loss: 1.1588 - val_accuracy: 0.6400\nEpoch 100/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0863 - accuracy: 0.5958 - val_loss: 0.8593 - val_accuracy: 0.7200\nEpoch 101/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.0582 - accuracy: 0.6075 - val_loss: 1.0562 - val_accuracy: 0.6933\nEpoch 102/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.0994 - accuracy: 0.5867 - val_loss: 0.9842 - val_accuracy: 0.7067\nEpoch 103/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.1147 - accuracy: 0.5850 - val_loss: 0.9333 - val_accuracy: 0.7000\nEpoch 104/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0928 - accuracy: 0.5925 - val_loss: 0.9704 - val_accuracy: 0.6700\nEpoch 105/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0875 - accuracy: 0.5925 - val_loss: 1.0071 - val_accuracy: 0.6833\nEpoch 106/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.0493 - accuracy: 0.6200 - val_loss: 0.9771 - val_accuracy: 0.6867\nEpoch 107/150\n38/38 [==============================] - 1s 14ms/step - loss: 1.0808 - accuracy: 0.6050 - val_loss: 1.0842 - val_accuracy: 0.6900\nEpoch 108/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.0564 - accuracy: 0.6175 - val_loss: 0.9190 - val_accuracy: 0.7067\nEpoch 109/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0919 - accuracy: 0.6167 - val_loss: 0.8960 - val_accuracy: 0.7033\nEpoch 110/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0562 - accuracy: 0.6075 - val_loss: 0.9280 - val_accuracy: 0.7200\nEpoch 111/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0799 - accuracy: 0.5908 - val_loss: 1.0617 - val_accuracy: 0.6667\nEpoch 112/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0593 - accuracy: 0.5975 - val_loss: 1.0082 - val_accuracy: 0.6833\nEpoch 113/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0566 - accuracy: 0.6075 - val_loss: 0.9210 - val_accuracy: 0.7033\nEpoch 114/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0439 - accuracy: 0.6158 - val_loss: 1.0210 - val_accuracy: 0.6833\nEpoch 115/150\n","name":"stdout"},{"output_type":"stream","text":"38/38 [==============================] - 1s 15ms/step - loss: 1.0187 - accuracy: 0.6233 - val_loss: 0.9173 - val_accuracy: 0.7133\nEpoch 116/150\n38/38 [==============================] - 1s 21ms/step - loss: 1.0374 - accuracy: 0.6233 - val_loss: 0.9648 - val_accuracy: 0.7100\nEpoch 117/150\n38/38 [==============================] - 1s 16ms/step - loss: 0.9700 - accuracy: 0.6292 - val_loss: 1.0015 - val_accuracy: 0.7033\nEpoch 118/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0033 - accuracy: 0.6325 - val_loss: 0.8813 - val_accuracy: 0.7267\nEpoch 119/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0219 - accuracy: 0.6175 - val_loss: 0.8755 - val_accuracy: 0.7133\nEpoch 120/150\n38/38 [==============================] - 1s 16ms/step - loss: 0.9844 - accuracy: 0.6308 - val_loss: 0.9077 - val_accuracy: 0.7200\nEpoch 121/150\n38/38 [==============================] - 1s 16ms/step - loss: 1.0132 - accuracy: 0.6408 - val_loss: 1.1385 - val_accuracy: 0.6300\nEpoch 122/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9923 - accuracy: 0.6317 - val_loss: 0.8960 - val_accuracy: 0.7367\nEpoch 123/150\n38/38 [==============================] - 1s 15ms/step - loss: 1.0024 - accuracy: 0.6233 - val_loss: 0.9550 - val_accuracy: 0.7100\nEpoch 124/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9990 - accuracy: 0.6325 - val_loss: 0.9834 - val_accuracy: 0.7133\nEpoch 125/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9789 - accuracy: 0.6492 - val_loss: 1.0492 - val_accuracy: 0.6733\nEpoch 126/150\n38/38 [==============================] - 1s 23ms/step - loss: 0.9877 - accuracy: 0.6475 - val_loss: 0.8614 - val_accuracy: 0.7200\nEpoch 127/150\n38/38 [==============================] - 1s 16ms/step - loss: 0.9913 - accuracy: 0.6433 - val_loss: 0.9783 - val_accuracy: 0.7167\nEpoch 128/150\n38/38 [==============================] - 1s 14ms/step - loss: 0.9867 - accuracy: 0.6425 - val_loss: 0.8281 - val_accuracy: 0.7367\nEpoch 129/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9829 - accuracy: 0.6467 - val_loss: 1.2210 - val_accuracy: 0.6333\nEpoch 130/150\n38/38 [==============================] - 1s 16ms/step - loss: 0.9985 - accuracy: 0.6458 - val_loss: 0.8398 - val_accuracy: 0.7200\nEpoch 131/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9613 - accuracy: 0.6467 - val_loss: 0.7964 - val_accuracy: 0.7567\nEpoch 132/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9906 - accuracy: 0.6408 - val_loss: 0.9728 - val_accuracy: 0.6767\nEpoch 133/150\n38/38 [==============================] - 1s 25ms/step - loss: 0.9628 - accuracy: 0.6433 - val_loss: 0.8599 - val_accuracy: 0.7267\nEpoch 134/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9732 - accuracy: 0.6525 - val_loss: 0.8743 - val_accuracy: 0.7300\nEpoch 135/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9790 - accuracy: 0.6467 - val_loss: 0.8044 - val_accuracy: 0.7167\nEpoch 136/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9281 - accuracy: 0.6600 - val_loss: 0.8851 - val_accuracy: 0.7333\nEpoch 137/150\n38/38 [==============================] - 1s 14ms/step - loss: 0.9526 - accuracy: 0.6525 - val_loss: 0.9637 - val_accuracy: 0.7233\nEpoch 138/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9437 - accuracy: 0.6417 - val_loss: 0.9272 - val_accuracy: 0.6933\nEpoch 139/150\n38/38 [==============================] - 1s 14ms/step - loss: 0.9130 - accuracy: 0.6633 - val_loss: 0.8890 - val_accuracy: 0.7267\nEpoch 140/150\n38/38 [==============================] - 1s 14ms/step - loss: 0.9552 - accuracy: 0.6533 - val_loss: 0.9884 - val_accuracy: 0.6933\nEpoch 141/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9495 - accuracy: 0.6375 - val_loss: 0.8599 - val_accuracy: 0.7267\nEpoch 142/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9872 - accuracy: 0.6358 - val_loss: 0.8439 - val_accuracy: 0.7267\nEpoch 143/150\n38/38 [==============================] - 1s 26ms/step - loss: 0.9359 - accuracy: 0.6658 - val_loss: 1.0342 - val_accuracy: 0.6800\nEpoch 144/150\n38/38 [==============================] - 1s 17ms/step - loss: 0.9244 - accuracy: 0.6692 - val_loss: 0.9188 - val_accuracy: 0.7233\nEpoch 145/150\n38/38 [==============================] - 1s 14ms/step - loss: 0.9477 - accuracy: 0.6625 - val_loss: 0.9100 - val_accuracy: 0.7200\nEpoch 146/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.8923 - accuracy: 0.6758 - val_loss: 0.8943 - val_accuracy: 0.7100\nEpoch 147/150\n38/38 [==============================] - 1s 16ms/step - loss: 0.9316 - accuracy: 0.6483 - val_loss: 0.8584 - val_accuracy: 0.7300\nEpoch 148/150\n38/38 [==============================] - 1s 16ms/step - loss: 0.9797 - accuracy: 0.6400 - val_loss: 0.8662 - val_accuracy: 0.7200\nEpoch 149/150\n38/38 [==============================] - 1s 15ms/step - loss: 0.9227 - accuracy: 0.6683 - val_loss: 0.8941 - val_accuracy: 0.7233\nEpoch 150/150\n38/38 [==============================] - 1s 31ms/step - loss: 0.9247 - accuracy: 0.6667 - val_loss: 1.1351 - val_accuracy: 0.6767\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.legend(['train','test'])\nplt.title('accuracy')","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"Text(0.5, 1.0, 'accuracy')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Training on the whole dataset based on the choosen parameter\nbatch_size = 32\n\ndatagen = ImageDataGenerator(rotation_range=30, width_shift_range=.2, \n                             height_shift_range=.2,vertical_flip=True,\n                             fill_mode='nearest',horizontal_flip=True,zoom_range=0.1,shear_range = 0.2 )\n\n# fit parameters from data\ndatagen.fit(train_x)\n\n#test_model --> based on the hypertuned parameters, we call the model\nhistory = test_model.fit_generator(datagen.flow(train_x, train_y , batch_size=batch_size),\n#                         steps_per_epoch=X_train.shape[0] // batch_size,\n                        epochs=120\n#                         validation_data=(X_validation, y_validation)#,callbacks=[early_stopping] \n                             )","execution_count":22,"outputs":[{"output_type":"stream","text":"Epoch 1/120\n47/47 [==============================] - 1s 16ms/step - loss: 0.9202 - accuracy: 0.6847\nEpoch 2/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9440 - accuracy: 0.6567\nEpoch 3/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9442 - accuracy: 0.6593\nEpoch 4/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9381 - accuracy: 0.6640\nEpoch 5/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9277 - accuracy: 0.6753\nEpoch 6/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9549 - accuracy: 0.6473\nEpoch 7/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9187 - accuracy: 0.6593\nEpoch 8/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9211 - accuracy: 0.6667\nEpoch 9/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.9048 - accuracy: 0.6913\nEpoch 10/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.9158 - accuracy: 0.6587\nEpoch 11/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8803 - accuracy: 0.6787\nEpoch 12/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8585 - accuracy: 0.7020\nEpoch 13/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.9120 - accuracy: 0.6720\nEpoch 14/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.9081 - accuracy: 0.6787\nEpoch 15/120\n47/47 [==============================] - 1s 20ms/step - loss: 0.8644 - accuracy: 0.6893\nEpoch 16/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8972 - accuracy: 0.6853\nEpoch 17/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8938 - accuracy: 0.6820\nEpoch 18/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8898 - accuracy: 0.6813\nEpoch 19/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8682 - accuracy: 0.7007\nEpoch 20/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8582 - accuracy: 0.7013\nEpoch 21/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8822 - accuracy: 0.6860\nEpoch 22/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8827 - accuracy: 0.6827\nEpoch 23/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8553 - accuracy: 0.6920\nEpoch 24/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8646 - accuracy: 0.6933\nEpoch 25/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8253 - accuracy: 0.7060\nEpoch 26/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8616 - accuracy: 0.6940\nEpoch 27/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8489 - accuracy: 0.7047\nEpoch 28/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8410 - accuracy: 0.7007\nEpoch 29/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8547 - accuracy: 0.6793\nEpoch 30/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8243 - accuracy: 0.7133\nEpoch 31/120\n47/47 [==============================] - 1s 19ms/step - loss: 0.8294 - accuracy: 0.7040\nEpoch 32/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8138 - accuracy: 0.7080\nEpoch 33/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8734 - accuracy: 0.6913\nEpoch 34/120\n47/47 [==============================] - 1s 15ms/step - loss: 0.8083 - accuracy: 0.7147\nEpoch 35/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8126 - accuracy: 0.7120\nEpoch 36/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8124 - accuracy: 0.7087\nEpoch 37/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8242 - accuracy: 0.7173\nEpoch 38/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8681 - accuracy: 0.6867\nEpoch 39/120\n47/47 [==============================] - 1s 22ms/step - loss: 0.8257 - accuracy: 0.7013\nEpoch 40/120\n47/47 [==============================] - 1s 15ms/step - loss: 0.8326 - accuracy: 0.7100\nEpoch 41/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7806 - accuracy: 0.7280\nEpoch 42/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8079 - accuracy: 0.7073\nEpoch 43/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8192 - accuracy: 0.6980\nEpoch 44/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8328 - accuracy: 0.7153\nEpoch 45/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8242 - accuracy: 0.7047\nEpoch 46/120\n47/47 [==============================] - 1s 27ms/step - loss: 0.7932 - accuracy: 0.7200\nEpoch 47/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8152 - accuracy: 0.7100\nEpoch 48/120\n47/47 [==============================] - 1s 15ms/step - loss: 0.7674 - accuracy: 0.7313\nEpoch 49/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.8191 - accuracy: 0.7213\nEpoch 50/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8135 - accuracy: 0.7187\nEpoch 51/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7855 - accuracy: 0.7253\nEpoch 52/120\n47/47 [==============================] - 1s 15ms/step - loss: 0.7668 - accuracy: 0.7313\nEpoch 53/120\n47/47 [==============================] - 1s 18ms/step - loss: 0.7971 - accuracy: 0.7260\nEpoch 54/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7648 - accuracy: 0.7320\nEpoch 55/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.8075 - accuracy: 0.7320\nEpoch 56/120\n47/47 [==============================] - 1s 15ms/step - loss: 0.7752 - accuracy: 0.7300\nEpoch 57/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7588 - accuracy: 0.7327\nEpoch 58/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7827 - accuracy: 0.7347\nEpoch 59/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7713 - accuracy: 0.7280\nEpoch 60/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7699 - accuracy: 0.7367\nEpoch 61/120\n47/47 [==============================] - 1s 21ms/step - loss: 0.7741 - accuracy: 0.7300\nEpoch 62/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7815 - accuracy: 0.7353\nEpoch 63/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7667 - accuracy: 0.7293\nEpoch 64/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7414 - accuracy: 0.7440\nEpoch 65/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7450 - accuracy: 0.7413\nEpoch 66/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7553 - accuracy: 0.7320\nEpoch 67/120\n47/47 [==============================] - 1s 15ms/step - loss: 0.7206 - accuracy: 0.7560\nEpoch 68/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7773 - accuracy: 0.7280\nEpoch 69/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7509 - accuracy: 0.7227\nEpoch 70/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7158 - accuracy: 0.7460\nEpoch 71/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7356 - accuracy: 0.7453\nEpoch 72/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7513 - accuracy: 0.7347\nEpoch 73/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7523 - accuracy: 0.7507\nEpoch 74/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7526 - accuracy: 0.7560\nEpoch 75/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7469 - accuracy: 0.7353\nEpoch 76/120\n47/47 [==============================] - 1s 18ms/step - loss: 0.7411 - accuracy: 0.7420\nEpoch 77/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7029 - accuracy: 0.7687\nEpoch 78/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7376 - accuracy: 0.7400\nEpoch 79/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7138 - accuracy: 0.7467\nEpoch 80/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7454 - accuracy: 0.7453 0s - loss: 0.7814 \nEpoch 81/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7201 - accuracy: 0.7453\nEpoch 82/120\n","name":"stdout"},{"output_type":"stream","text":"47/47 [==============================] - 1s 15ms/step - loss: 0.7318 - accuracy: 0.7407\nEpoch 83/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7274 - accuracy: 0.7467\nEpoch 84/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7341 - accuracy: 0.7507\nEpoch 85/120\n47/47 [==============================] - 1s 22ms/step - loss: 0.7081 - accuracy: 0.7547\nEpoch 86/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.7289 - accuracy: 0.7620\nEpoch 87/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7158 - accuracy: 0.7520\nEpoch 88/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6982 - accuracy: 0.7700\nEpoch 89/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6521 - accuracy: 0.7660\nEpoch 90/120\n47/47 [==============================] - 1s 12ms/step - loss: 0.7128 - accuracy: 0.7593\nEpoch 91/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7107 - accuracy: 0.7647\nEpoch 92/120\n47/47 [==============================] - 1s 19ms/step - loss: 0.7040 - accuracy: 0.7513\nEpoch 93/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7458 - accuracy: 0.7500\nEpoch 94/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7151 - accuracy: 0.7493\nEpoch 95/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6960 - accuracy: 0.7660 0s - loss: 0.6341 \nEpoch 96/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6900 - accuracy: 0.7753\nEpoch 97/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6981 - accuracy: 0.7520\nEpoch 98/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6660 - accuracy: 0.7673\nEpoch 99/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6789 - accuracy: 0.7707\nEpoch 100/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6733 - accuracy: 0.7640\nEpoch 101/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6369 - accuracy: 0.7873\nEpoch 102/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6722 - accuracy: 0.7467\nEpoch 103/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.7032 - accuracy: 0.7627\nEpoch 104/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6721 - accuracy: 0.7773\nEpoch 105/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6700 - accuracy: 0.7680\nEpoch 106/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6663 - accuracy: 0.7693\nEpoch 107/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6911 - accuracy: 0.7560\nEpoch 108/120\n47/47 [==============================] - 1s 19ms/step - loss: 0.6867 - accuracy: 0.7640\nEpoch 109/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6448 - accuracy: 0.7793\nEpoch 110/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6668 - accuracy: 0.7713\nEpoch 111/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6720 - accuracy: 0.7607\nEpoch 112/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6816 - accuracy: 0.7580\nEpoch 113/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6807 - accuracy: 0.7687\nEpoch 114/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6374 - accuracy: 0.7793\nEpoch 115/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6777 - accuracy: 0.7547\nEpoch 116/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6427 - accuracy: 0.7920 0s - loss: 0.6119 - \nEpoch 117/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6675 - accuracy: 0.7733\nEpoch 118/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6632 - accuracy: 0.7773\nEpoch 119/120\n47/47 [==============================] - 1s 13ms/step - loss: 0.6380 - accuracy: 0.7880\nEpoch 120/120\n47/47 [==============================] - 1s 14ms/step - loss: 0.6327 - accuracy: 0.7767\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Normalizing the values of the test data\ntest_x=test_x/255.0\n\n#Reshaping the data based on the training set shape\ntest_x = test_x.reshape(test_x.shape[0], 28, 28, 1)\n\n# using the predict function and taking the argmax to the class value\ny_pred = test_model.predict(test_x)\npred_label = np.argmax(y_pred, axis=-1)\npred_label.shape","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"(60000,)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Writing the result to the submission file\nimport pandas as pd\ni=list(range(0,len(pred_label)))\nmy_submission = pd.DataFrame({'Id': i, 'Category': pred_label})\nmy_submission.to_csv('submission11.csv', index=False)","execution_count":31,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_label[0:100]","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"array([2, 2, 4, 0, 4, 2, 2, 4, 5, 0, 3, 1, 1, 1, 5, 3, 2, 3, 1, 3, 0, 5,\n       4, 2, 3, 2, 5, 1, 0, 5, 2, 5, 5, 2, 5, 1, 5, 0, 0, 5, 5, 1, 0, 3,\n       0, 1, 2, 5, 2, 2, 0, 3, 1, 4, 2, 5, 2, 3, 2, 5, 1, 3, 3, 3, 2, 1,\n       0, 1, 5, 0, 0, 4, 1, 3, 0, 5, 2, 3, 3, 5, 5, 2, 4, 2, 5, 2, 0, 3,\n       5, 5, 0, 4, 1, 3, 2, 3, 4, 1, 0, 5])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}