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April 6, 2019 12:49
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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[ ]: | |
| import tensorflow as tf | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Activation, Dropout, Flatten,Conv2D, MaxPooling2D | |
| from keras.layers.normalization import BatchNormalization | |
| model = Sequential() | |
| model.add(Conv2D(filters=96, input_shape=(224,224,3), kernel_size=(11,11),strides=(4,4), padding='valid')) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid')) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid')) | |
| model.add(Activation('relu')) | |
| model.add(BatchNormalization()) | |
| model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid')) | |
| model.add(Activation('relu')) | |
| model.add(BatchNormalization()) | |
| model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid')) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Flatten()) | |
| model.add(Dense(4096, input_shape=(224*224*3,))) | |
| model.add(Activation('relu')) | |
| model.add(Dropout(0.4)) | |
| model.add(BatchNormalization()) | |
| model.add(Dense(4096)) | |
| model.add(Activation('relu')) | |
| model.add(Dropout(0.4)) | |
| model.add(BatchNormalization()) | |
| model.add(BatchNormalization()) | |
| model.add(Activation('relu')) | |
| model.add(Dropout(0.4)) | |
| model.add(BatchNormalization()) | |
| model.add(Dense(17)) | |
| model.add(Activation(tf.nn.softmax)) | |
| model.summary() | |
| model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| # model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| from keras.preprocessing.image import ImageDataGenerator | |
| train_datagen = ImageDataGenerator( | |
| rescale=1./255, | |
| shear_range=0.2, | |
| zoom_range=0.2, | |
| horizontal_flip=True) | |
| test_datagen = ImageDataGenerator(rescale=1./255) | |
| trainSet = train_datagen.flow_from_directory('/Users/macbook/Documents/ImportantStuff/lol/Train/', | |
| target_size=(224, 224), | |
| batch_size=32, | |
| class_mode='binary') | |
| testSet = test_datagen.flow_from_directory('/Users/macbook/Documents/ImportantStuff/lol/test/', | |
| target_size=(224, 224), | |
| batch_size=32, | |
| class_mode='binary') | |
| model.fit_generator(trainSet, | |
| steps_per_epoch=1705, | |
| epochs=7, | |
| validation_data=testSet, | |
| validation_steps=359) | |
| # In[3]: | |
| model.save('ALexNet.h5') | |
| model.save_weights('AlexNetWithWeights.h5') | |
| # In[5]: | |
| keras.callbacks.History() | |
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