10 examples of 'load model keras' in Python

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80def load_model():
81 """
82 """
83
84 json_file = open('multilabel_model.json', 'r')
85 loaded_model_json = json_file.read()
86 json_file.close()
87 model = model_from_json(loaded_model_json)
88
89 model.load_weights('multilabel_model.h5')
90 print("Loaded model from disk")
91
92 model.summary()
93
94 model.compile(loss='binary_crossentropy',
95 optimizer='adam',
96 metrics=[f1_score])
97
98
99 return model
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43def loaded_model():
44
45 json_file = open('/Users/yang/ChemTS/RNN-model/model.json', 'r')
46 #json_file = open('/Users/yang/LSTM-chemical-project/protein-ligand/model.json', 'r')
47 loaded_model_json = json_file.read()
48 json_file.close()
49 loaded_model = model_from_json(loaded_model_json)
50
51 # load weights into new model
52 #loaded_model.load_weights('/Users/yang/LSTM-chemical-project/protein-ligand/model.h5')
53 loaded_model.load_weights('/Users/yang/ChemTS/RNN-model/model.h5')
54 print("Loaded model from disk")
55
56
57 return loaded_model
140def load(self, model_folder='./model/'):
141 # load json and create model
142 json_file = open(model_folder + 'model.json', 'r')
143 loaded_model_json = json_file.read()
144 json_file.close()
145 loaded_model = model_from_json(loaded_model_json)
146 # load weights into new model
147 loaded_model.load_weights(model_folder + 'model.h5')
148 print('Loaded model from disk')
149
150 self.model = loaded_model
151 # loaded model should be compiled
152 self.__compile()
153 self.load_activation_model()
58def load(self, save_path):
59 self.classifier = keras.models.load_model(save_path)
120def load_pretrained_model():
121 weathernet = keras.models.load_model(model_filepath)
122 return weathernet
111def load_best_model(self):
112 print('Logging Info - Loading model checkpoint: %s.hdf5\n' % self.config.exp_name)
113 self.load_model(os.path.join(self.config.checkpoint_dir, '{}.hdf5'.format(self.config.exp_name)))
114 print('Logging Info - Model loaded')
103def _load_model(self):
104 '''
105 Loads the model weights from disk. Prepares the model to be able to
106 make predictions.
107 '''
108 self.logger.info(
109 'Loading model weights from {}'.format(self.model_filepath))
110 self.model = load_model(self.model_filepath)
111 self.graph = tf.get_default_graph()
4def ModelLoader(model_file):
5 print("Loading pre-trained model")
6 custom_objects = {'weighted_dice_coefficient_loss': weighted_dice_coefficient_loss}
7 try:
8 from keras_contrib.layers import InstanceNormalization
9 custom_objects["InstanceNormalization"] = InstanceNormalization
10 except ImportError:
11 pass
12 try:
13 return load_model(model_file, custom_objects=custom_objects)
14 except ValueError as error:
15 if 'InstanceNormalization' in str(error):
16 raise ValueError(str(error) + "\n\nPlease install keras-contrib to use InstanceNormalization:\n"
17 "'pip install git+https://www.github.com/keras-team/keras-contrib.git'")
18 else:
19 raise error
175def load_model(self):
176 print("BertBiLstmModel load_model start!")
177 # logger.info("BertBiLstmModel load_model start!")
178 self.model.load_weights(args.path_save_model)
179 # logger.info("BertBiLstmModel load_model end+!")
180 print("BertBiLstmModel load_model end+!")
282def load(model_name, img_dim, nb_patch, bn_mode, use_mbd, batch_size):
283
284 if model_name == "generator_unet_upsampling":
285 model = generator_unet_upsampling(img_dim, bn_mode, model_name=model_name)
286 model.summary()
287 return model
288
289 if model_name == "generator_unet_deconv":
290 model = generator_unet_deconv(img_dim, bn_mode, batch_size, model_name=model_name)
291 model.summary()
292 return model
293
294 if model_name == "DCGAN_discriminator":
295 model = DCGAN_discriminator(img_dim, nb_patch, bn_mode, model_name=model_name, use_mbd=use_mbd)
296 model.summary()
297 return model

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