Updated Chapter01

Chapter01/keras_MINST_V1.py

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+from __future__ import print_function
+import numpy as np
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.optimizers import SGD
+from keras.utils import np_utils
+
+np.random.seed(1671)  # for reproducibility
+
+# network and training
+NB_EPOCH = 200
+BATCH_SIZE = 128
+VERBOSE = 1
+NB_CLASSES = 10   # number of outputs = number of digits
+OPTIMIZER = SGD() # SGD optimizer, explained later in this chapter
+N_HIDDEN = 128
+VALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATION
+
+# data: shuffled and split between train and test sets
+#
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
+RESHAPED = 784
+#
+X_train = X_train.reshape(60000, RESHAPED)
+X_test = X_test.reshape(10000, RESHAPED)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# normalize 
+#
+X_train /= 255
+X_test /= 255
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
+Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
+
+# 10 outputs
+# final stage is softmax
+
+model = Sequential()
+model.add(Dense(NB_CLASSES, input_shape=(RESHAPED,)))
+model.add(Activation('softmax'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=OPTIMIZER,
+              metrics=['accuracy'])
+
+history = model.fit(X_train, Y_train,
+                    batch_size=BATCH_SIZE, epochs=NB_EPOCH,
+                    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
+score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
+print("\nTest score:", score[0])
+print('Test accuracy:', score[1])

Chapter01/keras_MINST_V2.py

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+from __future__ import print_function
+import numpy as np
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.optimizers import SGD
+from keras.utils import np_utils
+
+np.random.seed(1671)  # for reproducibility
+
+# network and training
+NB_EPOCH = 20
+BATCH_SIZE = 128
+VERBOSE = 1
+NB_CLASSES = 10   # number of outputs = number of digits
+OPTIMIZER = SGD() # optimizer, explained later in this chapter
+N_HIDDEN = 128
+VALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATION
+
+# data: shuffled and split between train and test sets
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
+RESHAPED = 784
+#
+X_train = X_train.reshape(60000, RESHAPED)
+X_test = X_test.reshape(10000, RESHAPED)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# normalize 
+X_train /= 255
+X_test /= 255
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
+Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
+
+# M_HIDDEN hidden layers
+# 10 outputs
+# final stage is softmax
+
+model = Sequential()
+model.add(Dense(N_HIDDEN, input_shape=(RESHAPED,)))
+model.add(Activation('relu'))
+model.add(Dense(N_HIDDEN))
+model.add(Activation('relu'))
+model.add(Dense(NB_CLASSES))
+model.add(Activation('softmax'))
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=OPTIMIZER,
+              metrics=['accuracy'])
+
+history = model.fit(X_train, Y_train,
+                    batch_size=BATCH_SIZE, epochs=NB_EPOCH,
+                    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
+
+score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
+print("\nTest score:", score[0])
+print('Test accuracy:', score[1])

Chapter01/keras_MINST_V3.py

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+from __future__ import print_function
+import numpy as np
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.optimizers import SGD
+from keras.utils import np_utils
+
+import matplotlib.pyplot as plt
+
+np.random.seed(1671)  # for reproducibility
+
+# network and training
+NB_EPOCH = 250
+BATCH_SIZE = 128
+VERBOSE = 1
+NB_CLASSES = 10   # number of outputs = number of digits
+OPTIMIZER = SGD() # optimizer, explained later in this chapter
+N_HIDDEN = 128
+VALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATION
+DROPOUT = 0.3
+
+# data: shuffled and split between train and test sets
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
+RESHAPED = 784
+#
+X_train = X_train.reshape(60000, RESHAPED)
+X_test = X_test.reshape(10000, RESHAPED)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# normalize 
+X_train /= 255
+X_test /= 255
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
+Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
+
+# M_HIDDEN hidden layers
+# 10 outputs
+# final stage is softmax
+
+model = Sequential()
+model.add(Dense(N_HIDDEN, input_shape=(RESHAPED,)))
+model.add(Activation('relu'))
+model.add(Dropout(DROPOUT))
+model.add(Dense(N_HIDDEN))
+model.add(Activation('relu'))
+model.add(Dropout(DROPOUT))
+model.add(Dense(NB_CLASSES))
+model.add(Activation('softmax'))
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=OPTIMIZER,
+              metrics=['accuracy'])
+
+history = model.fit(X_train, Y_train,
+                    batch_size=BATCH_SIZE, epochs=NB_EPOCH,
+                    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
+
+score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
+print("\nTest score:", score[0])
+print('Test accuracy:', score[1])
+
+# list all data in history
+print(history.history.keys())
+# summarize history for accuracy
+plt.plot(history.history['acc'])
+plt.plot(history.history['val_acc'])
+plt.title('model accuracy')
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+# summarize history for loss
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()

Chapter01/keras_MINST_V4.py

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+from __future__ import print_function
+import numpy as np
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.optimizers import RMSprop
+from keras.utils import np_utils
+
+import matplotlib.pyplot as plt
+
+np.random.seed(1671)  # for reproducibility
+
+# network and training
+NB_EPOCH = 20
+BATCH_SIZE = 128
+VERBOSE = 1
+NB_CLASSES = 10   # number of outputs = number of digits
+OPTIMIZER = RMSprop() # optimizer, explainedin this chapter
+N_HIDDEN = 128
+VALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATION
+DROPOUT = 0.3
+
+# data: shuffled and split between train and test sets
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
+RESHAPED = 784
+#
+X_train = X_train.reshape(60000, RESHAPED)
+X_test = X_test.reshape(10000, RESHAPED)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# normalize 
+X_train /= 255
+X_test /= 255
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
+Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
+
+# M_HIDDEN hidden layers
+# 10 outputs
+# final stage is softmax
+
+model = Sequential()
+model.add(Dense(N_HIDDEN, input_shape=(RESHAPED,)))
+model.add(Activation('relu'))
+model.add(Dropout(DROPOUT))
+model.add(Dense(N_HIDDEN))
+model.add(Activation('relu'))
+model.add(Dropout(DROPOUT))
+model.add(Dense(NB_CLASSES))
+model.add(Activation('softmax'))
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=OPTIMIZER,
+              metrics=['accuracy'])
+
+history = model.fit(X_train, Y_train,
+                    batch_size=BATCH_SIZE, epochs=NB_EPOCH,
+                    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
+
+score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
+print("\nTest score:", score[0])
+print('Test accuracy:', score[1])
+
+# list all data in history
+print(history.history.keys())
+# summarize history for accuracy
+plt.plot(history.history['acc'])
+plt.plot(history.history['val_acc'])
+plt.title('model accuracy')
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+# summarize history for loss
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()