Question

我正在为我自己的数据修改深度mnist代码。我修改了一个模型，但我面临一些基本的问题，比如我将数据逐个传递给我的模型，并且它快速运行reall但是当我通过我的模型所有的例子时，它变得非常慢，我也得到0％的准确性。请仔细检查我的代码我正在做一些可怕的错误，但我不知道我应该遵循哪些步骤和哪些步骤来使其正确。

这是我的模特

def deepnn(x):
"""deepnn builds the graph for a deep net for classifying digits.
Args:
x: an input tensor with the dimensions (N_examples, 784), where 784 is the
number of pixels in a standard MNIST image.
Returns:
A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
equal to the logits of classifying the digit into one of 10 classes (the
digits 0-9). keep_prob is a scalar placeholder for the probability of
dropout.
"""

x_image = tf.reshape(x, [-1, 28, 28, 1])


W_conv1 = weight_variable([5, 5, 1, 200])
b_conv1 = bias_variable([200])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)


h_pool1 = max_pool_2x2(h_conv1)


W_conv2 = weight_variable([5, 5, 200, 100])
b_conv2 = bias_variable([100])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

h_pool2 = max_pool_2x2(h_conv2)



W_fc1 = weight_variable([7 * 7 * 100, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*100])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 19])
b_fc2 = bias_variable([19])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
return y_conv, keep_prob

以下是我的模型所称的功能。

def conv2d(x, W):
"""conv2d returns a 2d convolution layer with full stride."""
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
"""max_pool_2x2 downsamples a feature map by 2X."""
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                    strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
"""weight_variable generates a weight variable of a given shape."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)


def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

这是我的主要

def main(_):

x = tf.placeholder(tf.float32, [None, 784]) 

y_ = tf.placeholder(tf.float32, [None, 19]) 
y_conv, keep_prob = deepnn(x)

cross_entropy   tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(34670):
            #batch = mnist.train.next_batch(50)
            if i % 1000 == 0:
                train_accuracy = accuracy.eval(feed_dict={x:     np.reshape(input_to_nn(i),(-1,784)), y_:np.reshape(output_of_nn(i),(-1,19)), keep_prob: 1.0})
                print('step %d, training accuracy %g' % (i, train_accuracy))
        train_step.run(feed_dict={x: np.reshape(input_to_nn(i),(-1,784)), y_:np.reshape(output_of_nn(i),(-1,19)), keep_prob: 0.5})

        print('test accuracy %g' % accuracy.eval(feed_dict={x:input_nn, y_:output_nn, keep_prob: 1.0}))

Answer 1

我认为问题出在以下几个方面：

W_fc2 = weight_variable([1024, 19])
b_fc2 = bias_variable([19])

你的模型训练预测19个班级。通常有10个数字，如果你真的没有19个类的图像，最好将值恢复为原来的10个。

Tensorflow中的模型不是工作需要审查代码不确定什么是错误的

1 个答案: