3 examples of 'keras plot model' in Python

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46def save_model_graph(self):
47 plot_model(self.Discriminator, to_file='/data/Discriminator_Model.png')
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25def visualize_model():
26 model = load_model('shallowCNN_model.h5') #See 'How to export keras models?' to generate this file before loading it.
27 # plot_model(model, to_file='shallowCNN_model.png')
28 model.summary()
131def train(self, x_data):
132 x_data, x_data = super().train(x_data, x_data)
133
134 x_mu_std = np.vstack((np.median(x_data), np.std(x_data)))
135 np.save(self.fullfilepath + 'meanstd_x.npy', x_mu_std)
136 x_data -= x_mu_std[0]
137 x_data /= x_mu_std[1]
138
139 csv_logger = CSVLogger(self.fullfilepath + 'log.csv', append=True, separator=',')
140
141 if self.task == 'classification':
142 raise RuntimeError('astroNN VAE does not support classification task')
143
144 reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, epsilon=self.reduce_lr_epsilon,
145 patience=self.reduce_lr_patience, min_lr=self.reduce_lr_min, mode='min',
146 verbose=2)
147 self.compile()
148 self.plot_model()
149
150 training_generator = DataGenerator(x_data.shape[1], self.batch_size).generate(x_data)
151
152 self.keras_model.fit_generator(generator=training_generator, steps_per_epoch=x_data.shape[0] // self.batch_size,
153 epochs=self.max_epochs, max_queue_size=20, verbose=2, workers=os.cpu_count(),
154 callbacks=[reduce_lr, csv_logger])
155
156 astronn_model = 'model_weights.h5'
157 self.keras_vae.save_weights(self.fullfilepath + astronn_model)
158 print(astronn_model + ' saved to {}'.format(self.fullfilepath + astronn_model))
159
160 K.clear_session()
161
162 return None

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