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April 6, 2019 12:46
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| # coding: utf-8 | |
| # In[1]: | |
| from keras import Sequential | |
| import os | |
| import sys as system | |
| # In[2]: | |
| model = Sequential() | |
| # In[3]: | |
| Image = system.argv[1] | |
| from keras.layers import Dense, Activation, Dropout, Flatten,Conv2D, MaxPooling2D | |
| from keras.layers.normalization import BatchNormalization | |
| 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('softmax')) | |
| # model.summary() | |
| model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| # In[63]: | |
| import numpy as np | |
| import keras.preprocessing.image as image | |
| # x = np.array(([ 'c:/lol/test/notSweet/badS176.jpg' , 'c:/lol/test/notSweet/badS176.jpg' , 'c:/lol/test/notSweet/badS205.jpg' , 'c:/lol/test/notSweet/notSweetS101.jpg'])) | |
| x = Image | |
| # In[25]: | |
| def categorizeModel(x): | |
| for value in range(len(x)): | |
| if x[value] == 0: | |
| print("notSweet") | |
| else: | |
| print("sweet") | |
| def load_image(img_path, show=False): | |
| img = image.load_img(img_path, target_size=(224, 224)) | |
| img_tensor = image.img_to_array(img) # (height, width, channels) | |
| img_tensor = np.expand_dims(img_tensor, axis=0) # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels) | |
| img_tensor /= 255. # imshow expects values in the range [0, 1] | |
| if show: | |
| plt.imshow(img_tensor[0]) | |
| plt.axis('off') | |
| plt.show() | |
| return img_tensor | |
| # In[64]: | |
| y0 = load_image(x) | |
| # y1 = load_image(x[1]) | |
| # y2 = load_image(x[2]) | |
| # y3 = load_image(x[3]) | |
| # In[65]: | |
| images = np.vstack([y0]) | |
| # In[66]: | |
| model.load_weights(os.path.abspath('//Users//macbook//Documents//ALexNet.h5')) | |
| # In[67]: | |
| categorizeModel(model.predict_classes(images , batch_size=10)) | |
| # In[68]: |
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