activation='relu',
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)(model)
model = Dense(4096,
activation='relu',
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)(model)
model = Dense(num_classes,
activation='softmax',
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)(model)
# Create model
model = Model(inputs=[model_input], outputs=[model])
# Load base model's weights
if model_params['base_model'] is not None:
model.load_weights(os.path.join(PROJECT_HOME, model_params['base_model']))
# ------------------------------------------------- Run training -------------------------------------------------
# Initialize training API
trainer = NeuralTrainer(model=model,
dirs=dirs,
logging_params=logging_params,
pipeline_params=pipeline_params,
training_params=training_params)
# Run training
trainer.initialize().run()
print('shape of test data after preprocessing: ', test_data)
train_data = train_data.shuffle(1000)
"""# Saving and Recreating the trained model"""
## Save the whole model
model.save('./trained_CNN/Smart_Truck/my_model_tld1.h5')
## Recreate whole model
new_model=keras.models.load_model('./trained_CNN/Smart_Truck/my_model_tld1.h5')
new_model.summary()
## Save the weights
model.save_weights('./trained_CNN/Smart_Truck/my_weights_tld1.h5')
## Restore the weights
model=create_model()
model.load_weights('./trained_CNN/Smart_Truck/my_weights_tld1.h5')
"""# DOWNLOAD created files
In this case downloading the previously created model.
Steps for downloading files manually: Anzeigen -> Inhalt -> Dateien (you can also display and download everything generated).
"""
from google.colab import files
files.download('./trained_CNN/Smart_Truck/my_model_tld1.h5')
