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akash3317 · Sep 29, 2019 · f520d5e · f520d5e
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diff --git a/ClassificationWithLinearRegression.ipynb b/ClassificationWithLinearRegression.ipynb
diff --git a/ClassificationWithLogisticRegression.ipynb b/ClassificationWithLogisticRegression.ipynb
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diff --git a/images/1dgd-bigstep.mp4 b/images/1dgd-bigstep.mp4
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diff --git a/images/gdline.mp4 b/images/gdline.mp4
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diff --git a/keras_dropout.py b/keras_dropout.py
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+# from wandb classes
+import numpy
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Dropout
+from keras.utils import np_utils
+import json
+
+from wandb.keras import WandbCallback
+import wandb
+
+run = wandb.init()
+config = run.config
+config.optimizer = "adam"
+config.epochs = 50
+config.dropout = 0.4
+config.hidden_nodes = 100
+
+# load data
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+img_width = X_train.shape[1]
+img_height = X_train.shape[2]
+
+X_train = X_train.astype('float32')
+X_train /= 255.
+X_test = X_test.astype('float32')
+X_test /= 255.
+
+# one hot encode outputs
+y_train = np_utils.to_categorical(y_train)
+y_test = np_utils.to_categorical(y_test)
+labels = range(10)
+
+num_classes = y_train.shape[1]
+
+# create model
+model=Sequential()
+model.add(Flatten(input_shape=(img_width,img_height)))
+model.add(Dropout(config.dropout))
+model.add(Dense(config.hidden_nodes, activation='relu'))
+model.add(Dropout(config.dropout))
+model.add(Dense(num_classes, activation='softmax'))
+model.compile(loss='categorical_crossentropy', optimizer=config.optimizer,
+                    metrics=['accuracy'])
+
+
+# Fit the model
+model.fit(X_train, y_train, validation_data=(X_test, y_test),
+        epochs=config.epochs, callbacks=[WandbCallback(data_type="image", labels=labels)])