from fdl_examples.datatools import input_data
mnist = input_data.read_data_sets("../../data/", one_hot=True)
import tensorflow as tf
import numpy as np
from fdl_examples.chapter3.multilayer_perceptron_updated import inference, loss
import matplotlib.pyplot as plt
sess = tf.Session()
x = tf.placeholder("float", [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder("float", [None, 10]) # 0-9 digits recognition => 10 classes
saver = tf.train.import_meta_graph('frozen_mlp_checkpoint/model-checkpoint-547800.meta')
saver.restore(sess, 'frozen_mlp_checkpoint/model-checkpoint-547800')
var_list_opt = [None, None, None, None, None, None]
name_2_index = {
"mlp_model/hidden_1/W:0" : 0,
"mlp_model/hidden_1/b:0" : 1,
"mlp_model/hidden_2/W:0" : 2,
"mlp_model/hidden_2/b:0" : 3,
"mlp_model/output/W:0" : 4,
"mlp_model/output/b:0" : 5
}
for x in tf.trainable_variables():
if x.name in name_2_index:
index = name_2_index[x.name]
Project 2 - Network A - "Normal" MNIST data (ie not rotated or scaled)
"""
import sys
sys.path.append('../../')
sys.path.append('../')
import numpy as np
import tensorflow as tf
import time, shutil, os
from fdl_examples.datatools import input_data
import matplotlib.pyplot as plt
# read in MNIST data --------------------------------------------------
mnist = input_data.read_data_sets("../../data/", one_hot=True)
# run network ----------------------------------------------------------
# Parameters
learning_rate = 0.01
training_epochs = 5 # NOTE: you'll want to eventually change this
batch_size = 100
display_step = 1
def inference(x, W, b):
output = tf.nn.softmax(tf.matmul(x, W) + b)
w_hist = tf.summary.histogram("weights", W)
b_hist = tf.summary.histogram("biases", b)
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Test various optimization strategies')
parser.add_argument('n_code', type=int)
args = parser.parse_args()
n_code = args.n_code
log_dir = "mnist_autoencoder_hidden={}_logs/".format(n_code)
ckpt = tf.train.get_checkpoint_state(log_dir)
savepath = ckpt.model_checkpoint_path
print("Use savepath: {}".format(savepath))
print("\nPULLING UP MNIST DATA")
mnist = input_data.read_data_sets("data/", one_hot=False)
print(mnist.test.labels)
# Apply PCA
print("\nSTARTING PCA")
pca = decomposition.PCA(n_components=n_code)
pca.fit(mnist.train.images)
print("\nGENERATING PCA CODES AND RECONSTRUCTION")
pca_codes = pca.transform(mnist.test.images)
with tf.Graph().as_default():
with tf.variable_scope("autoencoder_model"):
x = tf.placeholder("float", [None, 784])
phase_train = tf.placeholder(tf.bool)
code = ae.encoder(x, n_code, phase_train)
output = ae.decoder(code, n_code, phase_train)
