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61 lines
3.0 KiB
Python
61 lines
3.0 KiB
Python
4 years ago
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import tensorflow as tf
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import numpy as np
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from tensorflow.keras.callbacks import CSVLogger
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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mnist = tf.keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
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x_train = x_train / 255.0
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x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
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x_test = x_test / 255.0
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#y_train = tf.keras.utils.to_categorical(y_train)
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y_test = tf.keras.utils.to_categorical(y_test)
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def get_random_sample(a, b, number_of_samples=10):
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x = []
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y = []
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for category_number in range(0,10):
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# get all samples of a category
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train_data_category = a[b==category_number]
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# pick a number of random samples from the category
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train_data_category = train_data_category[np.random.randint(train_data_category.shape[0],
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size=number_of_samples), :]
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x.extend(train_data_category)
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y.append([category_number]*number_of_samples)
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return np.asarray(x).reshape(-1, 28, 28, 1), np.asarray(y).reshape(10*number_of_samples,1)
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for j in [0.0]:
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for i in ['1','2','3','4','5','6','7','8','9','0']:
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model = tf.keras.models.Sequential()
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model.add(tf.keras.layers.Conv2D(24,kernel_size=5,padding='same',activation='relu',
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input_shape=(28,28,1)))
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model.add(tf.keras.layers.MaxPool2D())
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model.add(tf.keras.layers.Conv2D(64,kernel_size=5,padding='same',activation='relu'))
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model.add(tf.keras.layers.MaxPool2D(padding='same'))
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model.add(tf.keras.layers.Flatten())
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model.add(tf.keras.layers.Dense(256, activation='relu'))
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model.add(tf.keras.layers.Dropout(j))
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model.add(tf.keras.layers.Dense(10, activation='softmax'))
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model.compile(optimizer='adam', loss="categorical_crossentropy", metrics=["accuracy"])
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print(model.summary())
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for n in [10,100]:
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x_train_, y_train_ = get_random_sample(x_train, y_train, number_of_samples=n)
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y_train_ = tf.keras.utils.to_categorical(y_train_)
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datagen = ImageDataGenerator(
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rotation_range = 30,
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zoom_range = 0.15,
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width_shift_range=2,
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height_shift_range=2,
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shear_range = 1)
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#x_test_ = np.append(x_train[300:],x_test).reshape(x_train[300:].shape[0]+x_test.shape[0],28,28,1)
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#y_test_ = np.append(y_train[300:],y_test).reshape(y_train[300:].shape[0]+y_test.shape[0],10)
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# csv_logger = CSVLogger('Sample/adam_dropout_'+str(j).replace('.',"")+'_'+str(n)+'_'+i+'.log')
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# history = model.fit(datagen.flow(x_train_, y_train_, batch_size=50), validation_data=(x_test, y_test), epochs=125, callbacks=[csv_logger], steps_per_epoch = x_train_.shape[0]//50)
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# history = model.fit(x_train_, y_train_, validation_data=(x_test, y_test), epochs=125, callbacks=[csv_logger])
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