Upload lesson 8

Author: ngjingheng95

Diff for: L8 Sonar Data Prep.py

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+#import libraries
+import numpy
+import pandas
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.cross_validation import cross_val_score
+from sklearn.preprocessing import LabelEncoder
+from sklearn.cross_validation import StratifiedKFold
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+
+seed = 7
+numpy.random.seed(seed)
+
+#load data
+dataframe = pandas.read_csv("sonar.csv", header = None)
+dataset = dataframe.values
+# split into input (X) and output (Y)
+X = dataset[:,0:60].astype(float)
+Y = dataset[:,60]
+
+# one hot encoding
+encoder = LabelEncoder()
+encoder.fit(Y)
+encoded_Y = encoder.transform(Y)
+
+#baseline model
+def create_baseline():
+    #create model
+    #we start with same number of neurons as input in hidden layer as a starting point
+    model = Sequential()
+    model.add(Dense(60, input_dim = 60, init = 'normal', activation = 'relu'))
+    model.add(Dense(1, init = 'normal', activation= 'sigmoid'))
+    #compile model
+    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])
+    return model
+
+#evaluating the baseline model with standardized dataset
+estimators = []
+estimators.append(('standardize', StandardScaler()))
+estimators.append(('mlp', KerasClassifier(build_fn=create_baseline, nb_epoch=100, batch_size = 5, verbose = 0)))
+pipeline = Pipeline(estimators)
+kfold = StratifiedKFold(y = encoded_Y, n_folds = 10, shuffle = True, random_state = seed)
+results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
+print("Standardized: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))