{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\nimport tensorflow as tf\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"X = np.load('/kaggle/input/ai-club-inductions/X.npy')\nX = X/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y = np.load('/kaggle/input/ai-club-inductions/Y.npy')\nX_test = np.load ('/kaggle/input/ai-club-inductions/X.npy')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dictionary = {\n0: 'T-Shirt',\n1: 'Shorts',\n2: 'Shoes',\n3: 'Dress',\n4: 'Blazers',\n5: 'Sandals',\n6: 'Bag' ,\n7: 'Skirt'\n}","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.layers import Input,Flatten,Dense\nfrom tensorflow.keras.models import Model\ndef model(shape,num_units1):\n    input_tensor = Input(shape) #input layer\n    flatten1 = Flatten()(input_tensor) #flatten\n    dense1 = Dense(num_units1,activation='relu')(flatten1) #hidden dense layer\n    dense2 = Dense(7,activation='softmax')(dense1) #Final output layer\n    model = Model(input_tensor,dense2) #model instance\n    return model \nmodel = model((28,28),128)\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.optimizers import Adam\nmodel.compile(optimizer=Adam(0.001),loss='CategoricalCrossentropy',metrics='acc') #compile the model\nhistory = model.fit(X/255,Y,epochs=10,verbose=1,shuffle=True,batch_size = 32)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.Sequential([\n    tf.keras.layers.Flatten(input_shape=(28, 28)),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(7)\n])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X,Y,epochs=15)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\ndef viz_pred(image):\n    pred = model.predict(image.reshape((1,28,28))) #reshaping and setting the batch to1\n    label = np.argmax(pred) #getting the label\n    fig,ax =plt.subplots(1,1,figsize=(1,1),constrained_layout=True)\n    ax.imshow(image)\n    ax.axis('off')\n    ax.set_title(dictionary[label])\nviz_pred(X_test[3070])","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}