72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
import tensorflow as tf
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import tensorflow.keras.backend as K
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from tensorflow.keras.layers import *
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from tensorflow.keras.models import *
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from tensorflow.keras.optimizers import *
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from tensorflow.keras.callbacks import *
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from tensorflow.keras.layers import Input
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import numpy as np
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import os
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import zipfile
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def RMSE(y_true, y_pred):
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return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))
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def build_model():
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inp = Input(shape=(12,24,72,4))
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x_4 = Dense(1, activation='relu')(inp)
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x_3 = Dense(1, activation='relu')(tf.reshape(x_4,[-1,12,24,72]))
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x_2 = Dense(1, activation='relu')(tf.reshape(x_3,[-1,12,24]))
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x_1 = Dense(1, activation='relu')(tf.reshape(x_2,[-1,12]))
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x = Dense(64, activation='relu')(x_1)
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x = Dropout(0.25)(x)
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x = Dense(32, activation='relu')(x)
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x = Dropout(0.25)(x)
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output = Dense(24, activation='linear')(x)
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model = Model(inputs=inp, outputs=output)
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adam = tf.optimizers.Adam(lr=1e-3,beta_1=0.99,beta_2 = 0.99)
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model.compile(optimizer=adam, loss=RMSE)
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return model
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model = build_model()
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model.load_weights('./user_data/model_data/model_mlp_baseline.h5')
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test_path = './tcdata/enso_round1_test_20210201/'
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### 1. 测试数据读取
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files = os.listdir(test_path)
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test_feas_dict = {}
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for file in files:
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test_feas_dict[file] = np.load(test_path + file)
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### 2. 结果预测
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test_predicts_dict = {}
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for file_name,val in test_feas_dict.items():
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test_predicts_dict[file_name] = model.predict(val).reshape(-1,)
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# test_predicts_dict[file_name] = model.predict(val.reshape([-1,12])[0,:])
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### 3.存储预测结果
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for file_name,val in test_predicts_dict.items():
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np.save('./result/' + file_name,val)
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#打包目录为zip文件（未压缩）
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def make_zip(source_dir='./result/', output_filename = 'result.zip'):
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zipf = zipfile.ZipFile(output_filename, 'w')
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pre_len = len(os.path.dirname(source_dir))
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source_dirs = os.walk(source_dir)
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print(source_dirs)
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for parent, dirnames, filenames in source_dirs:
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print(parent, dirnames)
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for filename in filenames:
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if '.npy' not in filename:
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continue
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pathfile = os.path.join(parent, filename)
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arcname = pathfile[pre_len:].strip(os.path.sep) #相对路径
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zipf.write(pathfile, arcname)
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zipf.close()
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make_zip()
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