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- import tensorflow as tf
- from tensorflow.keras.datasets import cifar10
- from tensorflow.keras.preprocessing.image import ImageDataGenerator
- from tensorflow.keras.models import Sequential
- from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
- from tensorflow.keras.layers import Conv2D, MaxPooling2D
- import pickle
- #LOAD DATASETS
- pickle_in = open("X.pickle","rb")
- X = pickle.load(pickle_in)
- pickle_in = open("y.pickle","rb")
- y = pickle.load(pickle_in)
- X = X/255.0
- #CREATE MODEL
- model = Sequential()
- model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
- model.add(Activation('relu'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Conv2D(256, (3, 3)))
- model.add(Activation('relu'))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
- model.add(Dense(64))
- model.add(Dense(1))
- model.add(Activation('sigmoid'))
- model.compile(loss='binary_crossentropy',
- optimizer='adam',
- metrics=['accuracy'])
- #TRAIN MODEL
- model.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)
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