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32 lines
1003 B
32 lines
1003 B
import tensorflow as tf
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import csv
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new_model = tf.keras.models.load_model('model')
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# Check its architecture
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new_model.summary()
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train_label = []
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train_feature=[]
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with open("test.csv","r") as r:
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reader = csv.reader(r)
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for i in reader:
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train_label.append((eval(i.pop())))
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b = [eval(j) for j in i]
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train_feature.append(b)
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train_features = tf.constant(train_feature)
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train_labels = tf.constant(train_label)
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test_accuracy = tf.keras.metrics.Accuracy()
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branch = 32
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for num in range(len(train_features)//branch):
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# training=False is needed only if there are layers with different
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# behavior during training versus inference (e.g. Dropout).
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x = train_features[num*32:(num+1)*32]
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y = train_labels[num*32:(num+1)*32]
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logits = new_model(x, training=False)
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prediction = tf.argmax(logits, axis=1, output_type=tf.int32)
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print("真实值为",y,"预测值为",prediction)
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test_accuracy(prediction, y)
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print("Test set accuracy: {:.3%}".format(test_accuracy.result())) |