raphael
fbc6ee8187
- worse performance than with initial design
- vgg16 pretrained weights are used for the base
network, which is then piped into a custom head
model, which
- flattens the layer (previously done in the base model)
+ Dense Layer
+ Normalization
+ Activation
- training split with 360 fruits used, same as previous mode
- maximum prediction level around 0.95 after ca 60 epochs
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
"""
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This is a modified version of the Keras mnist example.
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https://keras.io/examples/mnist_cnn/
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Instead of using a fixed number of epochs this version continues to train
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until the stop criteria is reached.
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Model performance should be around 99.4% after training.
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This scripts shows how to correctly handle mnist data
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and how to use it for the model.fit() function
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"""
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from __future__ import print_function
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import keras
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from keras.datasets import mnist
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from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Activation
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from keras import backend as K
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from keras.callbacks import ModelCheckpoint, EarlyStopping
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from keras.models import Model
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from keras.layers import Input, Flatten, Dense
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batch_size = 128
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num_classes = 10
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epochs = 999999
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# input image dimensions
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img_rows, img_cols = 28, 28
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# the data, split between train and test sets
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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if K.image_data_format() == 'channels_first':
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x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
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x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
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input_shape = (1, img_rows, img_cols)
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else:
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x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
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x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
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input_shape = (img_rows, img_cols, 1)
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x_train = x_train.astype('float32')
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x_test = x_test.astype('float32')
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x_train /= 255
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x_test /= 255
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y_train = keras.utils.to_categorical(y_train, num_classes)
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y_test = keras.utils.to_categorical(y_test, num_classes)
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def create_base_network(input_shape):
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input = Input(shape=input_shape)
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x = Conv2D(32, kernel_size=(3, 3),
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input_shape=input_shape)(input)
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x = BatchNormalization()(x)
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x = Activation(activation='relu')(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Conv2D(64, kernel_size=(3, 3))(x)
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x = BatchNormalization()(x)
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x = Activation(activation='relu')(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Flatten()(x)
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x = Dense(128)(x)
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x = BatchNormalization()(x)
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x = Activation(activation='relu')(x)
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x = Dense(num_classes)(x)
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x = BatchNormalization()(x)
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x = Activation(activation='softmax')(x)
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return Model(input, x)
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model = create_base_network(input_shape)
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model.compile(loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.adam(),
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metrics=['accuracy'])
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checkpoint_path = "./checkpoint"
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callbacks = [
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EarlyStopping(monitor='val_acc', patience=10, verbose=0),
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ModelCheckpoint(checkpoint_path,
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monitor='val_acc',
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save_best_only=True,
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verbose=0)
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]
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=1,
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callbacks=callbacks,
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validation_data=(x_test, y_test))
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model.load_weights(checkpoint_path)
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score = model.evaluate(x_test, y_test, verbose=0)
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print('Test loss:', score[0])
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print('Test accuracy:', score[1])
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