10 examples of 'keras load model and predict' 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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45def load_model(
46 has_model: bool = True, model_path: str = f"{LATEST_STABLE_MODEL}"
47) -> LogisticRegression:
48 """
49 Load or create the logistic regression model.
50
51 Returns:
52 A logistic regression model either created from scratch or
53 loaded from a pickle file
54 """
55 # Load the model from memory or from a beautiful pickle file
56 if has_model:
57 lr_file = open(f"{ETC_DIR}/models/{model_path}", "rb")
58 model = pickle.load(lr_file)
59 lr_file.close()
60 LOGGER.info(f"Loaded model: {model_path}")
61 else:
62 lr_file = open(f"{ETC_DIR}/models/{model_path}", "wb")
63 model = create_lr()
64 pickle.dump(model, lr_file)
65 lr_file.close()
66 LOGGER.info(f"Created and saved: {model_path}")
67
68 return model
120def load_pretrained_model():
121 weathernet = keras.models.load_model(model_filepath)
122 return weathernet
58def load(self, save_path):
59 self.classifier = keras.models.load_model(save_path)
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
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
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+!")
207def load(path, opts, vars):
208 try:
209 print('\nLoading model\nCreating session and graph')
210 server = tf.train.Server.create_local_server()
211 sess = tf.Session(server.target)
212 graph = tf.get_default_graph()
213 backend.set_session(sess)
214
215 model_path = path + '.' + opts['network'] + '.h5'
216 print('Loading model from {}'.format(model_path))
217 model = load_model(model_path);
218
219
220 print('Create prediction function')
221
222 model._make_predict_function()
223 with graph.as_default():
224 with sess.as_default():
225 input_shape = list(model.layers[0].input_shape)
226 input_shape[0] = 1
227 model.predict(np.zeros(tuple(input_shape)))
228
229 vars['graph'] = graph
230 vars['session'] = sess
231 vars['model'] = model
232 except Exception as e:
233 print_exception(e, 'load')
234 sys.exit()
535def load_model_and_predict(self):
536 # load model
537 print('loading model ' + self.file_name + '.h5...')
538 model = load_model(os.path.join(self.file_path, 'model_' + self.file_name + '-' + 'seq_' + str(self.n_seq) + '.h5'))
539 # load dataset
540 series, series_values, raw_datetime = self.load_dataset()
541 # In order to make fake data, we need to random shuffle the values
542 # series, series_values = self._random_shuffle(series)
543 # n_test = int(0.2 * series.shape[0])
544 n_test = 30
545 scaler, train, test = self.prepare_data(series_values, n_test, self.n_lag, self.n_seq)
546 # make a prediction
547 forecasts = self.make_forecasts(model, self.n_batch, test, self.n_lag, self.n_seq)
548 # inverse transform forecasts and test pyplot.show()
549
550 forecasts = self.inverse_transform(series_values, forecasts, scaler, n_test + self.n_seq - 1)
551 # map forecasts to a health score
552 # self.get_health_score(raw_datetime, forecasts, n_test)
553
554 actual = [row[self.n_lag:] for row in test]
555 actual = self.inverse_transform(series_values, actual, scaler, n_test + self.n_seq - 1)
556 # evaluate forecasts
557 self.evaluate_forecasts(actual, forecasts, self.n_lag, self.n_seq, self.file_name)
558 # plot forecasts
559 # self.plot_forecasts(series_values, forecasts, n_test, self.file_name, self.sensor_name, raw_datetime, self.n_seq)
560 self._plot(series_values, forecasts, n_test, self.file_name, self.sensor_name, raw_datetime, self.n_seq)
50def load_model(self):
51 depth=Input(shape=(TIME_STEPS,hd,wd,1),name='depth_flow')
52 opflow=Input(shape=(TIME_STEPS,ho,wo,2),name='optical_flow')
53 cnv1=TimeDistributed(Conv2D(64, (7,7),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(depth)
54 cnv1_2=TimeDistributed(Conv2D(64, (7,7),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv1)
55 cnv2=TimeDistributed(Conv2D(128, (5,5),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(opflow)
56 cnv2_2=TimeDistributed(Conv2D(128, (5,5),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv2)
57 merged=concatenate([cnv1_2,cnv2_2])
58 cnv3=TimeDistributed(Conv2D(256, (5,5),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(merged)
59 cnv3_1=TimeDistributed(Conv2D(256, (3,3),strides=1, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv3)
60 cnv4=TimeDistributed(Conv2D(512, (3,3),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv3_1)
61 cnv4_1=TimeDistributed(Conv2D(512, (3,3),strides=1, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv4)
62 cnv5=TimeDistributed(Conv2D(512, (3,3),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv4_1)
63 cnv5_1=TimeDistributed(Conv2D(512, (3,3),strides=1, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv5)
64 cnv6=TimeDistributed(Conv2D(1024, (3,3),strides=2, padding='same',dilation_rate=(1, 1), activation='relu', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros',kernel_regularizer=regularizers.l2(0.01),bias_regularizer=regularizers.l2(0.01),activity_regularizer=regularizers.l2(0.01)))(cnv5_1)
65 drp_1=TimeDistributed(Dropout(0.5))(cnv6)
66 flat=TimeDistributed(Flatten())(drp_1)
67 lstm1=LSTM(LSTM_HIDDEN_SIZE,return_sequences=True)(flat)
68 lstm2=LSTM(6,return_sequences=True,name='output')(lstm1)
69 self.model=Model(inputs=[depth,opflow],outputs=[lstm2])
70 adm = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
71 self.model.compile(optimizer=adm,
72 loss=self.loss_modified)
73 self.model.summary()
74 if(not os.path.isdir(self.checkpoint_dir)):
75 os.makedirs(self.checkpoint_dir)

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