from utils import plot_utils
from utils import pruning_utils
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
#mnist = input_data.read_data_sets("FASHION_MNIST_data/")
pruning_utils.cifar10toMnist(mnist)
test_data_provider = mnist.test
# create pruned classifier
classifier = network_dense.FullyConnectedClassifier(
input_size=config_pruned.input_size,
n_classes=config_pruned.n_classes,
layer_sizes=config_pruned.layer_sizes,
model_path=config_pruned.model_path,
dropout=config_pruned.dropout,
weight_decay=config_pruned.weight_decay,
activation_fn=config_pruned.activation_fn,
pruning_threshold=config_pruned.pruning_threshold)
q_classifier = network_dense.FullyConnectedClassifier(
input_size=config_pruned.input_size,
n_classes=config_pruned.n_classes,
layer_sizes=config_pruned.layer_sizes,
model_path=config_pruned.q_model_path,
dropout=config_pruned.dropout,
weight_decay=config_pruned.weight_decay,
activation_fn=config_pruned.activation_fn,
pruning_threshold=config_pruned.pruning_threshold)
# restore a model
import tensorflow as tf
tf.set_random_seed(123)
import numpy as np
np.random.seed(123)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
train_data_provider = mnist.train
validation_data_provider = mnist.validation
test_data_provider = mnist.test
from networks import network_dense
from configs import ConfigNetworkDense as config
# create a classifier
classifier = network_dense.FullyConnectedClassifier(input_size=config.input_size,
n_classes=config.n_classes,
layer_sizes=config.layer_sizes,
model_path=config.model_path,
dropout=config.dropout,
weight_decay=config.weight_decay,
activation_fn=config.activation_fn)
# than train it
classifier.fit(n_epochs=config.n_epochs,
batch_size=config.batch_size,
learning_rate_schedule=config.learning_rate_schedule,
train_data_provider=train_data_provider,
validation_data_provider=validation_data_provider,
test_data_provider=test_data_provider)
