tensorflow/ kears

Author: opliyal3

2019-11-23

@@ -0,0 +1,151 @@
+.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+

data/iris.data

@@ -0,0 +1,39 @@
+import numpy
+from keras.layers import Dense
+from keras.models import Sequential
+import os
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.model_selection import StratifiedKFold, cross_val_score, GridSearchCV
+
+print(os.getcwd())
+
+dataset1 = numpy.loadtxt('data\\diabetes.csv', delimiter=',', skiprows=1)
+print(type(dataset1), dataset1.shape)
+
+inputList = dataset1[:, 0:8]
+resultList = dataset1[:, 8]
+print(inputList[:5])
+print(numpy.unique(resultList))
+
+
+def create_default_model(optimizer='adam', init='uniform'): #1
+    model = Sequential()
+    model.add(Dense(14, input_dim=8, kernel_initializer=init, activation='relu')) # 2
+    model.add(Dense(12, activation='relu'))
+    model.add(Dense(1, activation='sigmoid'))
+    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) # 3
+    print(model.summary())
+    return model
+
+
+# , epochs=200, batch_size=20, ==> remove
+model = KerasClassifier(build_fn=create_default_model, verbose=0) # 4
+# 5
+optimizers = ['rmsprop', 'adam']
+inits = ['normal', 'uniform']
+epochs = [50, 100, 150]
+batches = [5, 10, 15]
+fiveFold = StratifiedKFold(n_splits=5, shuffle=True)
+
+result = cross_val_score(model, inputList, resultList, cv=fiveFold)
+print(f"result mean={result.mean()}, result std={result.std()}")

demo43.py

@@ -0,0 +1,36 @@
+import numpy as np
+from keras import models
+from keras import layers
+from keras.datasets import boston_housing
+
+(train_data, train_target), (test_data, test_target) = boston_housing.load_data()
+print(train_data.shape, test_data.shape)
+
+mean = train_data.mean(axis=0)
+train_data -= mean
+std = train_data.std(axis=0)
+train_data /= std
+test_data -= mean
+test_data /= std
+print(train_data.shape, test_data.shape)
+
+
+def build_model():
+    model = models.Sequential()
+    model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1],)))
+    model.add(layers.Dense(32, activation='relu'))
+    model.add(layers.Dense(1))
+    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
+    model.summary()
+    return model
+
+
+model = build_model()
+model.fit(train_data, train_target, validation_split=0.1, epochs=100, batch_size=10, verbose=1)
+
+# for item in test_target:
+#     print(item)
+
+for (i, j) in zip(test_data, test_target):
+    predict = model.predict(i.reshape(1, -1))
+    print(f'actual={j}, predict as={predict}')

demo44.py

@@ -0,0 +1,47 @@
+from keras import Sequential
+from keras.layers import Dense
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn import preprocessing
+from pandas import read_csv
+from sklearn.model_selection import KFold, cross_val_score
+from sklearn.preprocessing import LabelEncoder
+from keras.utils import np_utils
+
+dataFrame = read_csv('data\\iris.data', header=None)
+print(type(dataFrame), dataFrame.shape)
+print(dataFrame.columns)
+print(dataFrame.index)
+print(type(dataFrame.values))
+print(dataFrame.values)
+print(dataFrame.values[0,], type(dataFrame.values[0,][0]), type(dataFrame.values[0,][4]))
+
+# cut data
+dataset = dataFrame.values
+features = dataset[:, 0:4].astype(float)
+labels = dataset[:, 4]
+print(features.mean(axis=0))
+result = preprocessing.scale(features, axis=0, with_mean=True)
+print(result.mean(axis=0))
+
+encoder = LabelEncoder()
+encoder.fit(labels)
+encoded_Y = encoder.transform(labels)
+print(type(encoded_Y), encoded_Y[:10], encoded_Y[50:60], encoded_Y[100:110])
+dummy_y = np_utils.to_categorical(encoded_Y)
+print(type(dummy_y), dummy_y[:10], dummy_y[50:60], dummy_y[100:110])
+
+
+def baseline_model():
+    model = Sequential()
+    model.add(Dense(8, input_dim=4, activation='relu'))
+    model.add(Dense(12, activation='relu'))
+    model.add(Dense(3, activation='softmax'))
+    model.summary()
+    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    return model
+
+
+estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=10, verbose=1)
+Kfold = KFold(n_splits=3, shuffle=True)
+results = cross_val_score(estimator, features, dummy_y, cv=Kfold)
+print("accuracy: %.4f, std: %.4f"%(results.mean(), results.std()))

demo45.py

@@ -0,0 +1,20 @@
+scores = [3.0, 4.0, 3.0]
+import numpy as np
+
+
+def manualSoftmax(x):
+    x = np.array(x)
+    return np.exp(x) / np.sum(np.exp(x), axis=0)
+
+
+print(manualSoftmax(scores))
+
+import tensorflow as tf
+
+result2 = tf.nn.softmax(scores)
+print(result2)
+
+scores2 = [1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 1.0, 1.0]
+result3 = tf.nn.softmax(scores2)
+print(result3)
+

demo46.py

@@ -0,0 +1,25 @@
+import numpy
+from keras.datasets import imdb
+from matplotlib import pyplot
+
+(X_train, y_train), (X_test, y_test) = imdb.load_data()
+print(X_train.shape)
+print(X_test.shape)
+print(y_train.shape)
+print(y_test.shape)
+print(numpy.unique(y_train), numpy.unique(y_test))
+X = numpy.concatenate((X_train, X_test), axis=0)
+y = numpy.concatenate((y_train, y_test), axis=0)
+print(X[0])
+print(X.shape)
+print(y.shape)
+print(len(numpy.unique(numpy.hstack(X))))
+result = [len(x) for x in X]
+print(result[:50])
+print(f'comments mean={numpy.mean(result)}, std={numpy.std(result)}')
+
+pyplot.subplot(121)
+pyplot.boxplot(result)
+pyplot.subplot(122)
+pyplot.hist(result)
+pyplot.show()

demo47.py

@@ -0,0 +1,43 @@
+from keras import layers
+from keras import models
+from keras.datasets import imdb
+import numpy as np
+
+(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
+print(train_data[0])
+print(max([max(sequence) for sequence in train_data]))
+
+word_index = imdb.get_word_index()
+reverse_word_index = dict([(v, k) for k, v in word_index.items()])
+for k in range(5):
+    decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[k]])
+    print(decoded_review)
+print(train_labels[:5])
+
+
+def vectorize_sequence(sequences, dimension=10000):
+    results = np.zeros((len(sequences), dimension))
+    for i, sequence in enumerate(sequences):
+        results[i, sequence] = 1.
+    return results
+
+x_train = vectorize_sequence(train_data)
+x_test = vectorize_sequence(test_data)
+y_train = np.asarray(train_labels).astype('float32')
+y_test = np.asarray(test_labels).astype('float32')
+print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
+print(x_train[0])
+
+model = models.Sequential()
+model.add(layers.Dense(32, activation='relu', input_shape=(10000,)))
+model.add(layers.Dense(16, activation='relu'))
+model.add(layers.Dense(1, activation='sigmoid'))
+model.compile(optimizer='rmsprop', loss='binary_crossentropy',metrics=['accuracy'])
+model.summary()
+
+x_val = x_train[:10000]
+partial_x_train = x_train[10000:]
+y_val = y_train[:10000]
+partial_y_train = y_train[10000:]
+
+model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=128)