from cnn_toolbox import initialize_pseudo_random_number_generators,\
create_cnn_model,\
get_weights_from_conv_layer,\
train_cnn_complete,\
prepare_output_folder,\
save_history
# 4.1 set defined start value for random number generators?
#initialize_pseudo_random_number_generators( rnd_seed_value )
# 4.2 create the CNN
import tensorflow as tf
#with tf.device(device_name):
model = create_cnn_model(model_name="inc-nr-filters",
input_shape=img_shape,
nr_outputs=ds_train.nr_classes,
learn_rate=learn_rate)
model.summary()
# 4.3 plausiblity check whether the weights are really different
# for the case, a different random seed was used
filter_weights, bias_weights = get_weights_from_conv_layer(model,
"conv2d",
show_info=True)
print("Here are filter 0 weights:")
print(filter_weights[:, :, :, 0])
# 4.4 train the CNN completely
#with tf.device(device_name):
history = train_cnn_complete(your_cnn=model,
your_train_ds=ds_train,
# i.e. classification results on training and testing dataset
# after each epoch
from cnn_toolbox import initialize_pseudo_random_number_generators,\
create_cnn_model,\
get_weights_from_conv_layer,\
train_cnn_complete,\
prepare_output_folder,\
save_history
# 4.1 set start value for random number generation
initialize_pseudo_random_number_generators(rnd_seed_value)
# 4.2 create the CNN
model = create_cnn_model(model_name="same_nr_filters",
input_shape=img_shape,
nr_outputs=ds_train.nr_classes)
model.summary()
# 4.3 plausiblity check whether the weights are really different
filter_weights, bias_weights = get_weights_from_conv_layer(model,
"conv2d",
show_info=True)
print("Here are filter 0 weights:")
print(filter_weights[:, :, :, 0])
# 4.4 train the CNN completely
history = train_cnn_complete(model, ds_train, ds_test, stop_epochnr=100)
# 4.5 save training history for further later analysis
output_folder = "saved_model_histories"
from cnn_toolbox import initialize_pseudo_random_number_generators,\
create_cnn_model,\
get_weights_from_conv_layer,\
train_cnn_complete,\
prepare_output_folder,\
save_history
# 4.1 set defined start value for random number generators?
#initialize_pseudo_random_number_generators( rnd_seed_value )
# 4.2 create the CNN
import tensorflow as tf
#with tf.device(device_name):
model = create_cnn_model(model_name=cnn_name,
input_shape=img_shape,
nr_outputs=ds_train.nr_classes)
model.summary()
# 4.3 plausiblity check whether the weights are really different
# for the case, a different random seed was used
filter_weights, bias_weights = get_weights_from_conv_layer(model,
"conv2d",
show_info=True)
print("Here are filter 0 weights:")
print(filter_weights[:, :, :, 0])
# 4.4 train the CNN completely
#with tf.device(device_name):
history = train_cnn_complete(your_cnn=model,
your_train_ds=ds_train,