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我已經在培訓期間保存了這個模型，但我很難加載它並評估它。恢復和評估一個Tensorflow模型
我嘗試了一些不同的方法，但我無法加載保存的模型，並評估它獲得對某個測試樣本（它是圖像文件）的預測。
任何人都可以幫忙嗎？因爲我看到它似乎並不那麼困難，但我錯過了任何要做的事。
#!/usr/bin/python
import tensorflow as tf
BATCH_SIZE = 128
NUM_EXAMPLES_PER_EPOCH = 50000
VALIDATION_SIZE = 10000
WIDTH = 128
HEIGHT = 64
CHANNELS = 3
CLASSES = 10
NUMBERS = 4
def inference(inputs):
with tf.variable_scope("conv_pool_1"):
kernel = tf.get_variable(name="kernel",
shape=[5, 5, 3, 48],
initializer=tf.truncated_normal_initializer(stddev=0.05),
dtype=tf.float32)
biases = tf.get_variable(name="biases",
shape=[48],
initializer=tf.constant_initializer(value=0.),
dtype=tf.float32)
conv = tf.nn.conv2d(input=inputs,
filter=kernel,
strides=[1, 1, 1, 1],
padding="SAME")
conv_bias = tf.nn.bias_add(value=conv,
bias=biases,
name="add_biases")
relu = tf.nn.relu(conv_bias)
pool = tf.nn.max_pool(value=relu,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding="SAME",
name="pooling")
with tf.variable_scope("conv_pool_2"):
kernel = tf.get_variable(name="kernel",
shape=[5, 5, 48, 64],
initializer=tf.truncated_normal_initializer(stddev=0.05),
dtype=tf.float32)
biases = tf.get_variable(name="biases",
shape=[64],
initializer=tf.constant_initializer(value=0.),
dtype=tf.float32)
conv = tf.nn.conv2d(input=pool,
filter=kernel,
strides=[1, 1, 1, 1],
padding="SAME")
conv_bias = tf.nn.bias_add(value=conv,
bias=biases,
name="add_biases")
relu = tf.nn.relu(conv_bias)
pool = tf.nn.max_pool(value=relu,
ksize=[1, 2, 1, 1],
strides=[1, 2, 1, 1],
padding="SAME",
name="pooling")
with tf.variable_scope("conv_pool_3"):
kernel = tf.get_variable(name="kernel",
shape=[5, 5, 64, 128],
initializer=tf.truncated_normal_initializer(stddev=0.05),
dtype=tf.float32)
biases = tf.get_variable(name="biases",
shape=[128],
initializer=tf.constant_initializer(value=0.),
dtype=tf.float32)
conv = tf.nn.conv2d(input=pool,
filter=kernel,
strides=[1, 1, 1, 1],
padding="SAME")
conv_bias = tf.nn.bias_add(value=conv,
bias=biases,
name="add_biases")
relu = tf.nn.relu(conv_bias)
pool = tf.nn.max_pool(value=relu,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding="SAME",
name="pooling")
reshape = tf.reshape(pool,
shape=[BATCH_SIZE, -1],
name="reshape")
dims = reshape.get_shape().as_list()[-1]
with tf.variable_scope("fully_conn"):
weights = tf.get_variable(name="weights",
shape=[dims, 2048],
initializer=tf.truncated_normal_initializer(stddev=0.05),
dtype=tf.float32)
biases = tf.get_variable(name="biases",
shape=[2048],
initializer=tf.constant_initializer(value=0.),
dtype=tf.float32)
output = tf.nn.xw_plus_b(x=reshape,
weights=weights,
biases=biases)
conn = tf.nn.relu(output)
with tf.variable_scope("output"):
weights = tf.get_variable(name="weights",
shape=[2048, NUMBERS * CLASSES],
initializer=tf.truncated_normal_initializer(stddev=0.05),
dtype=tf.float32)
biases = tf.get_variable(name="biases",
shape=[NUMBERS * CLASSES],
initializer=tf.constant_initializer(value=0.),
dtype=tf.float32)
logits = tf.nn.xw_plus_b(x=conn,
weights=weights,
biases=biases)
reshape = tf.reshape(logits, shape=[BATCH_SIZE, NUMBERS, CLASSES])
return reshape
def loss(logits, labels):
cross_entropy_per_number = tf.nn.softmax_cross_entropy_with_logits(logits, labels)
cross_entropy = tf.reduce_mean(cross_entropy_per_number)
tf.add_to_collection("loss", cross_entropy)
return cross_entropy
def evaluation(logits, labels):
prediction = tf.argmax(logits, 2)
actual = tf.argmax(labels, 2)
equal = tf.equal(prediction, actual)
# equal = tf.reduce_all(equal, 1)
accuracy = tf.reduce_mean(tf.cast(equal, tf.float32), name="accuracy")
return accuracy
def train(loss, learning_rate=0.00001):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
return train_op