6 API DOC —— Python API
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切记:tensorflow中大小写非常有区别!!
3.2. 变量:创建、初始化、保存和加载:
tf.Variable
6.3.1 ARRAY OPS
tf.reshape(tensor, shape, name=None)
class tf.Session
tf.Session.run(fetches, feed_dict=None)
6.3.3. CONSTANT OP:
tf.random_uniform(shape, minval=0.0, maxval=1.0, dtype=tf.float32, seed=None, name=None)
tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)
tf.zeros(shape, dtype=tf.float32, name=None)
tf.constant(value, dtype=None, shape=None, name=\'Const\')
6.3.5 FRAMWORK
tf.name_scope(name)
tf.placeholder(dtype, shape=None, name=None)
6.3.9:Math Opt:
tf.reduce_mean
tf.square(x, name=None)
tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None)
tf.add(x, y, name=None)
tf.reduce_sum(input_tensor, reduction_indices=None, keep_dims=False, name=None)
tf.nn.relu(features, name=None)
tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)
tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)
tf.nn.max_pool(value, ksize, strides, padding, name=None)
tf.initialize_all_variables()
tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None)
tf.constant_initializer(value=0.0)
tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)
class tf.train.GradientDescentOptimizer(learning_rate=?)
Google:
tf.summary.histogram
3.2. 变量:创建、初始化、保存和加载:
class tf.Variable
http://www.tensorfly.cn/tfdoc/api_docs/python/array_ops.html#reshape
6.3.1 ARRAY OPS
tf.reshape(tensor, shape, name=None)
https://blog.csdn.net/lxg0807/article/details/53021859
http://www.tensorfly.cn/tfdoc/api_docs/python/array_ops.html#reshape
class tf.Session
tf.Session.run(fetches, feed_dict=None):
http://www.tensorfly.cn/tfdoc/api_docs/python/client.html#Session
6.3.3. CONSTANT OP:
tf.random_uniform(shape, minval=0.0, maxval=1.0, dtype=tf.float32, seed=None, name=None):
Outputs random values from a uniform distribution.(从均匀分布中输出随机值)
The generated values follow a uniform distribution in the range [minval, maxval)
. The lower bound minval
is included in the range, while the upper bound maxval
is excluded.
http://www.tensorfly.cn/tfdoc/api_docs/python/constant_op.html#random_uniform
tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)
mean
: A 0-D Tensor or Python value of typedtype
. The mean of the normal distribution(正态分布的均值)stddev
: A 0-D Tensor or Python value of typedtype
. The standard deviation of the normal distribution(正态分布的标准差).
http://www.tensorfly.cn/tfdoc/api_docs/python/constant_op.html#random_normal
tf.zeros(shape, dtype=tf.float32, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/constant_op.html#zeros
tf.constant(value, dtype=None, shape=None, name=\'Const\')
注意:上面tf.constant()中参数指出shape的必要性,参数对应!
参考链接:http://www.tensorfly.cn/tfdoc/api_docs/python/constant_op.html#constant
6.3.5 FRAMWORK
tf.name_scope(name)
http://www.tensorfly.cn/tfdoc/api_docs/python/framework.html#name_scope
tf.placeholder(dtype, shape=None, name=None)
Inserts a placeholder for a tensor that will be always fed.
参考链接:http://www.tensorfly.cn/tfdoc/api_docs/python/io_ops.html#placeholder
6.3.9:Math Opt:
tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None):
http://www.tensorfly.cn/tfdoc/api_docs/python/math_ops.html#reduce_mean
tf.square(x, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/math_ops.html#square
tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/math_ops.html#matmul
tf.add(x, y, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/math_ops.html#add
tf.reduce_sum(input_tensor, reduction_indices=None, keep_dims=False, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/math_ops.html#reduce_sum
tf.nn.relu(features, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/nn.html#relu
tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/nn.html#dropout
tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/nn.html#conv2d
tf.nn.max_pool(value, ksize, strides, padding, name=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/nn.html#max_pool
tf.initialize_all_variables()
http://www.tensorfly.cn/tfdoc/api_docs/python/state_ops.html#initialize_all_variables
tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/state_ops.html#random_normal_initializer
tf.constant_initializer(value=0.0)
http://www.tensorfly.cn/tfdoc/api_docs/python/state_ops.html#constant_initializer
class tf.train.Saver:
tf.train.Saver.save(sess, save_path, global_step=None, latest_filename=None)
tf.train.Saver.restore(sess, save_path)
http://www.tensorfly.cn/tfdoc/api_docs/python/state_ops.html#Saver
tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)
http://www.tensorfly.cn/tfdoc/api_docs/python/state_ops.html#get_checkpoint_state
class tf.train.GradientDescentOptimizer(learning_rate=?)
http://www.tensorfly.cn/tfdoc/api_docs/python/train.html#GradientDescentOptimizer
-----------------------------------------------------------------------------------------------------------------------------------
tf.summary.histogram
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