123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
import sys
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import os
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from keras.layers.pooling import MaxPool2D
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sys.path.append('c:/git/keras')
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sys.path.append('c:/git/absl')
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# installed
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# py -mpip install numpy
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# py -mpip show numpy
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# py -mpip install tensorflow
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# py -mpip show tensorflow
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# py -mpip install matplotlib
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# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
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import tensorflow
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import keras
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from keras.optimizers import adam_v2
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from tensorflow.keras.optimizers import Adam
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from keras.datasets import cifar10
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from keras.preprocessing.image import ImageDataGenerator
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from keras.models import Sequential
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from keras.utils import np_utils
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from keras.layers import Dense
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from keras.layers import Activation
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from keras.layers import Flatten
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from keras.layers import Dropout
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from keras.layers import BatchNormalization
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from keras.layers import Conv2D
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from keras.layers import MaxPooling2D
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from keras.layers import AveragePooling2D
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from keras.callbacks import EarlyStopping, ModelCheckpoint
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from keras import regularizers
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from keras import optimizers
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from matplotlib import pyplot
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from keras.callbacks import ModelCheckpoint
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from keras.callbacks import LearningRateScheduler
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from keras.preprocessing.image import load_img
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from keras.preprocessing.image import img_to_array
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from keras.preprocessing.image import array_to_img
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from keras.optimizer_v2 import gradient_descent
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#Load some data
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#(x_train,y_train), (x_test,y_test)=cifar10.load_data()
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base_filters=6
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image_size = (128, 128)
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batch_size = 256
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learning_rate=.00001
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epochs=1000
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patience_on_early_stop=10
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model_name='lenet5.h5'
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train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
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'C:\\boneyard\\DeepLearning\\data',
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label_mode="binary",
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subset="training",
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validation_split=.20,
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image_size=image_size,
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color_mode='grayscale',
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batch_size=64,
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seed=1337
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)
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val_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
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'C:\\boneyard\\DeepLearning\\data',
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label_mode="binary",
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subset="validation",
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validation_split=.20,
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image_size=image_size,
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color_mode='grayscale',
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batch_size=64,
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seed=1337
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)
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model=Sequential()
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#C1
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model.add(Conv2D(filters=base_filters,kernel_size=(3,3),strides=1,activation='relu',input_shape=(128,128,1),padding='same'))
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='valid'))
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model.add(BatchNormalization())
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#C2
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),strides=1,activation='relu',padding='valid'))
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='valid'))
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model.add(Dropout(.20))
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#C3
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model.add(Conv2D(filters=base_filters*16,kernel_size=(3,3),strides=1,activation='relu',padding='valid'))
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model.add(BatchNormalization())
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#C4
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model.add(Flatten())
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#FC6
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model.add(Dense(units=84,activation='relu'))
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#FC7
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model.add(Dense(units=1,activation='sigmoid'))
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model.summary()
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optimizer=gradient_descent.SGD(learning_rate=learning_rate, momentum=0.9, nesterov=False)
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checkpointer=ModelCheckpoint(filepath=model_name,monitor='accuracy',verbose=1,save_best_only=True)
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early_stopping=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=patience_on_early_stop)
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model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
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history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,callbacks=[checkpointer,early_stopping])
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# plot the learning curves
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pyplot.plot(history.history['accuracy'],label='train')
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pyplot.plot(history.history['val_accuracy'],label='validation')
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pyplot.legend()
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pyplot.show()
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