55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
#!/usr/bin/python3
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img_size = (150, 150)
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from tensorflow.keras import models as m
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from tensorflow.keras import layers as l
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from tensorflow.keras import optimizers as o
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from PIL import Image
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from sys import argv
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from os import listdir as ls
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import numpy as np
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model = m.Sequential([
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l.Input(shape = (*img_size, 3)),
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l.Conv2D(96, (11, 11), strides = 4, activation = "relu"),
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l.BatchNormalization(),
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l.MaxPooling2D((3, 3), strides = 2),
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l.Conv2D(192, (5, 5), activation = "relu", padding = "same"),
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l.BatchNormalization(),
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l.MaxPooling2D((3, 3), strides = 2),
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l.Conv2D(256, (3, 3), activation = "relu", padding = "same"),
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l.Conv2D(256, (3, 3), activation = "relu", padding = "same"),
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l.Conv2D(160, (3, 3), activation = "relu", padding = "same"),
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l.BatchNormalization(),
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l.MaxPooling2D((3, 3), strides = 2),
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l.Flatten(),
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l.Dense(1024, activation = "relu"),
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l.Dropout(0.5),
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l.Dense(1024, activation = "relu"),
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l.Dropout(0.5),
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l.Dense(10, activation = "softmax"),
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])
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model.compile(optimizer = o.Adam(learning_rate = 0.0001),
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loss = "categorical_crossentropy",
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metrics = ["accuracy"])
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model.load_weights("w2.weights.h5")
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if len(argv) >= 2:
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for i in argv[1:]:
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with Image.open(i) as im:
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im = im.resize((150, 150), Image.Resampling.LANCZOS)
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im = np.divide(np.array(im),
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np.array(255.))
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res = model.predict(np.array([im]))
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print(res)
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print(np.argmax(res))
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print(sorted(ls("../ds/raw-img"))[np.argmax(res)])
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