我正在尝试了解此错误的基础以及它所依赖的参数。 我需要尝试运行神经网络模型，并想使用过滤器数量和过滤器尺寸。
train_X = data.train.images.reshape(-1, 28, 28, 1)
test_X = data.test.images.reshape(-1,28,28,1)
train_y = data.train.labels
test_y = data.test.labels
print (train_X.shape, test_X.shape)
#____________________
training_iters = 200
learning_rate = 0.001
batch_size = 4
# MNIST data input (img shape: 28*28)
n_input = 28
# MNIST total classes (0-9 digits)
n_classes = 10
#both placeholders are of type float
x = tf.placeholder(tf.float32, [None, 28,28,1])
y = tf.placeholder(tf.float32,shape=[None,10])
#_____________
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')
#___________
tf.reset_default_graph()
weights = {
'wc1': tf.get_variable('W0', shape=(5,5,1,6), initializer=tf.contrib.layers.xavier_initializer()),
'wc2': tf.get_variable('W1', shape=(3,3,6,64), initializer=tf.contrib.layers.xavier_initializer()),
'wc3': tf.get_variable('W2', shape=(3,3,64,128), initializer=tf.contrib.layers.xavier_initializer()),
'wd1': tf.get_variable('W3', shape=(4*4*128,128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('W6', shape=(128,n_classes), initializer=tf.contrib.layers.xavier_initializer()),
}
biases = {
'bc1': tf.get_variable('B0', shape=(6), initializer=tf.contrib.layers.xavier_initializer()),
'bc2': tf.get_variable('B1', shape=(64), initializer=tf.contrib.layers.xavier_initializer()),
'bc3': tf.get_variable('B2', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'bd1': tf.get_variable('B3', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('B4', shape=(10), initializer=tf.contrib.layers.xavier_initializer()),
}
#__________________
def conv_net(x, weights, biases):
# here we call the conv2d function we had defined above and pass the input image x, weights wc1 and bias bc1.
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 14*14 matrix.
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
# here we call the conv2d function we had defined above and pass the input image x, weights wc2 and bias bc2.
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 7*7 matrix.
conv2 = maxpool2d(conv2, k=2)
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 4*4.
conv3 = maxpool2d(conv3, k=2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Output, class prediction
# finally we multiply the fully connected layer with the weights and add a bias term.
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
我正在尝试了解此错误的基础以及它所依赖的参数。 我需要尝试运行神经网络模型，并想使用过滤器数量和过滤器尺寸。