在这个CNN降维，似乎是想对我的理论

Question

我有一个两层CNN与以下建筑的理解：

下面是这个架构中tensorflow表示：

import os 
import tensorflow as tf 
import sys 
import urllib 
import numpy as np 
import random 
from sklearn.preprocessing import OneHotEncoder 
from PIL import Image 
import glob 
train = [] 
for filename in glob.glob('/Users/madhavthaker/Documents/CSCI63/Final Project/face-emoticon-master/data/ck+_scaled/*.png'): #assuming gif 
    img=np.asarray(Image.open(filename)) 
    img_flat = img.reshape(img.size) 
    train.append(img_flat) 

if sys.version_info[0] >= 3: 
    from urllib.request import urlretrieve 
else: 
    from urllib import urlretrieve 

LOGDIR = 'log3/' 
GITHUB_URL ='https://raw.githubusercontent.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/master/' 

### MNIST EMBEDDINGS ### 
ckp_labels = [5, 0, 3, 5, 4, 0, 1, 3, 5, 4, 0, 3, 5, 0, 1, 5, 4, 0, 0, 0, 2, 1, 3, 5, 0, 3, 5, 1, 3, 5, 0, 3, 5, 4, 0, 3, 5, 3, 1, 1, 0, 4, 5, 2, 1, 5, 3, 5, 1, 5, 3, 1, 5, 1, 5, 0, 1, 5, 3, 5, 1, 3, 0, 1, 5, 2, 3, 1, 5, 3, 1, 3, 1, 5, 3, 2, 5, 3, 1, 5, 3, 4, 0, 5, 0, 3, 1, 3, 2, 5, 1, 3, 5, 1, 5, 4, 0, 3, 1, 5, 1, 2, 5, 1, 3, 5, 3, 5, 1, 3, 5, 5, 3, 1, 1, 3, 4, 1, 5, 4, 1, 5, 0, 1, 3, 5, 2, 3, 5, 5, 3, 5, 1, 0, 1, 5, 3, 0, 5, 1, 0, 3, 5, 0, 3, 5, 3, 1, 4, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 5, 0, 3, 5, 1, 1, 4, 1, 5, 1, 4, 1, 0, 1, 3, 5, 5, 0, 1, 0, 5, 4, 0, 5, 3, 5, 3, 5, 1, 3, 5, 2, 0, 5, 2, 0, 5, 2, 3, 4, 3, 2, 5, 1, 5, 0, 3, 0, 1, 3, 5, 0, 1, 3, 5, 0, 4, 3, 3, 1, 4, 2, 1, 3, 5, 5, 3, 0, 3, 1, 5, 5, 0, 3, 5, 3, 2, 5, 3, 4, 7, 7, 7, 7, 7, 7, 7, 7, 0, 2, 4, 0, 7, 2, 0, 7, 0, 7, 2, 4, 4, 0, 2, 4, 7, 2] 
labels_test = np.array(ckp_labels).reshape(-1,1) 

enc = OneHotEncoder() 
enc.fit(labels_test) 
labels_final = enc.transform(labels_test).toarray() 

train = np.asarray(train) 

# Add convolution layer 
def conv_layer(input, size_in, size_out, name="conv"): 
    with tf.name_scope(name): 
    #w = tf.Variable(tf.zeros([5, 5, size_in, size_out]), name="W") 
    #b = tf.Variable(tf.zeros([size_out]), name="B") 
    w = tf.Variable(tf.truncated_normal([17, 17, size_in, size_out], stddev=0.1), name="W") 
    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B") 
    conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME") 
    act = tf.nn.relu(conv + b) 
    tf.summary.histogram("weights", w) 
    tf.summary.histogram("biases", b) 
    tf.summary.histogram("activations", act) 
    return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") 

# Add fully connected layer 
def fc_layer(input, size_in, size_out, name="fc"): 
    with tf.name_scope(name): 
    w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W") 
    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B") 
    act = tf.nn.relu(tf.matmul(input, w) + b) 
    tf.summary.histogram("weights", w) 
    tf.summary.histogram("biases", b) 
    tf.summary.histogram("activations", act) 
    return act 


def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam): 

    tf.reset_default_graph() 
    tf.set_random_seed(1) 
    sess = tf.Session() 

    # Setup placeholders, and reshape the data 
    x = tf.placeholder(tf.float32, shape=[None, 256*256], name="x") 
    x_image = tf.reshape(x, [-1, 256, 256, 1]) 
    tf.summary.image('input', x_image, 3) 
    y = tf.placeholder(tf.float32, shape=[None, 7], name="labels") 

    if use_two_conv: 
    conv1 = conv_layer(x_image, 1, 32, "conv1") 
    conv_out = conv_layer(conv1, 32, 64, "conv2") 
    else: 
    conv1 = conv_layer(x_image, 1, 64, "conv") 
    conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") #adding padding "VALID" means no padding 
    flattened = tf.reshape(conv_out, [-1, 55 * 55 * 64]) 


    if use_two_fc: 
    fc1 = fc_layer(flattened, 55 * 55 * 64, 40, "fc1") 
    embedding_input = fc1 
    embedding_size = 40 
    logits = fc_layer(fc1, 40, 7, "fc2") 
    else: 
    embedding_input = flattened 
    embedding_size = 7*7*64 
    logits = fc_layer(flattened, 7*7*64, 10, "fc") 

    with tf.name_scope("xent"): 
    xent = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(
      logits=logits, labels=y), name="xent") 
    tf.summary.scalar("xent", xent) 

    with tf.name_scope("train"): 
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent) 

    with tf.name_scope("accuracy"): 
    correct_prediction = tf.equal(tf.argmax(logits, -1), tf.argmax(y, -1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    tf.summary.scalar("accuracy", accuracy) 

    summ = tf.summary.merge_all() 


    embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding") 
    assignment = embedding.assign(embedding_input) 
    saver = tf.train.Saver() 

    sess.run(tf.global_variables_initializer()) 
    writer = tf.summary.FileWriter(LOGDIR + hparam) 
    writer.add_graph(sess.graph) 

    config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig() 
    embedding_config = config.embeddings.add() 
    embedding_config.tensor_name = embedding.name 
    embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png' 
    embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv' 
    # Specify the width and height of a single thumbnail. 
    embedding_config.sprite.single_image_dim.extend([256, 256]) 
    tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config) 

    for i in range(300): 
    batch_index = random.sample(range(0,100),25) 

    if i % 5 == 0: 
     [train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: train[batch_index], y: labels_final[batch_index]}) 
     writer.add_summary(s, i) 
     print ("train accuracy:", train_accuracy) 
    sess.run(train_step, feed_dict={x: train[batch_index], y: labels_final[batch_index]}) 

def make_hparam_string(learning_rate, use_two_fc, use_two_conv): 
    conv_param = "conv2" if use_two_conv else "conv1" 
    fc_param = "fc2" if use_two_fc else "fc1" 
    return "lr_%.0E%s%s" % (learning_rate, conv_param, fc_param) 

def main(): 
    # You can try adding some more learning rates 
    #for learning_rate in [1E-3, 1E-4, 1E-5]: 
    for learning_rate in [1E-4]: 

    # Include "False" as a value to try different model architectures 
    #for use_two_fc in [True, False]: 
    for use_two_fc in [True]: 
     #for use_two_conv in [True, False]: 
     for use_two_conv in [True]: 
     # Construct a hyperparameter string for each one (example: "lr_1E-3fc2conv2") 
     hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv) 
     print('Starting run for %s' % hparam) 
     sys.stdout.flush() # this forces print-ed lines to show up. 

     # Actually run with the new settings 
     mnist_model(learning_rate, use_two_fc, use_two_conv, hparam) 


if __name__ == '__main__': 
    main() 

根据我的数学，一切都检查出来，但是当我运行代码时，出现以下错误：

InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 6553600 values, but the requested shape requires a multiple of 193600 

我得到的错误在这行代码：

flattened = tf.reshape(conv_out, [-1, 55 * 55 * 64]) 

我真的不知道这是为什么发生。我的数学有问题，因为他们认为conv_out的展平尺寸应该是[-1, 64*64*64]。

任何帮助将不胜感激。让我知道你是否需要更多信息。

Answer 1

它看起来像我错误地计算了每个卷积/合并图层输出的大小。以下是你如何解决这个问题。我提炼你的代码下降到只有这个：

import tensorflow as tf 
import numpy as np 

def conv_layer(input, size_in, size_out, name="conv"): 
    with tf.name_scope(name): 
    w = tf.Variable(tf.truncated_normal([17, 17, size_in, size_out], stddev=0.1), name="W") 
    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B") 
    conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="VALID") 
    act = tf.nn.relu(conv + b) 
    return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") 


# Setup placeholders, and reshape the data 
x = tf.placeholder(tf.float32, shape=[None, 256*256], name="x") 
x_image = tf.reshape(x, [-1, 256, 256, 1]) 

conv1 = conv_layer(x_image, 1, 32, "conv1") 
conv_out = conv_layer(conv1, 32, 64, "conv2") 

flattened = tf.reshape(conv_out, [-1, 55 * 55 * 64]) 

sess.run(tf.global_variables_initializer()) 
print(sess.run(tf.shape(conv1), {x: np.zeros([1, 256*256])})) 

此代码提要正确的形状的输入为零，并使用tf.shape()计算conv1输出的形状。我回到：

[ 1 128 128 32]

这不符合您的计算数字。

我怀疑你错误地计算了填充，但很难说如果不知道你是如何得到顶部表格中的数字的话。如果没有别的，第一个卷积有填充SAME和步幅1，所以输入和输出将具有相同的空间维度。

希望这会有所帮助！