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Author: ngjingheng95

L9 Sonar Different Network Sizes.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
+
+#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)
+
+def create_smaller():
+    #create a network with less neurons in first hidden layer
+    # 60i - 30 - 1o
+    model = Sequential()
+    model.add(Dense(30, 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
+
+def create_larger():
+    #create a network with more hidden layers
+    # 60i - 60 - 30 - 1o
+    model = Sequential()
+    model.add(Dense(60, input_dim = 60, init = 'normal', activation = 'relu'))
+    model.add(Dense(30, init = 'normal', activation = 'relu'))
+    model.add(Dense(1, init = 'normal', activation = 'sigmoid'))
+    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
+    return model
+
+seed = 7
+numpy.random.seed(seed)
+
+#evaluating the smaller network
+estimatorsSmall = []
+estimatorsSmall.append(('standardize', StandardScaler()))
+estimatorsSmall.append(('mlp', KerasClassifier(build_fn = create_smaller, nb_epoch = 100, batch_size = 5, verbose = 0)))
+pipelineSmall = Pipeline(estimatorsSmall)
+kfold = StratifiedKFold(y = encoded_Y, n_folds = 10, shuffle = True, random_state = seed)
+resultsSmall = cross_val_score(pipelineSmall, X, encoded_Y, cv = kfold)
+
+#evaluating the larger network
+estimatorsLarge = []
+estimatorsLarge.append(('standardize', StandardScaler()))
+estimatorsLarge.append(('mlp', KerasClassifier(build_fn = create_larger, nb_epoch = 100, batch_size = 5, verbose = 0)))
+pipelineLarge = Pipeline(estimatorsLarge)
+kfold = StratifiedKFold(y = encoded_Y, n_folds = 10, shuffle = True, random_state = seed)
+resultsLarge = cross_val_score(pipelineLarge, X, encoded_Y, cv = kfold)
+
+# results
+print("Smaller: %.2f%% (%.2f%%)" % (resultsSmall.mean() * 100, resultsSmall.std() * 100))
+print("Larger: %.2f%% (%.2f%%)" % (resultsLarge.mean() * 100, resultsLarge.std() * 100))
+
+