A TensorFLow CheatSheet

Commonly used functions

• tf.saved_model.loader.load(sess, tags, export_dir)
• tf.get_default_graph() # Return the default Graph being used in the current thread.
• graph.get_tensor_by_name(tensor_name)
• tf.reshape(

  tensor, shape, name=None

  )

Define Layers

• tf.layers.conv2d(

      inputs,
      filters,
      kernel_size,
      strides=(1, 1),
      padding='valid',
      data_format='channels_last',
      dilation_rate=(1, 1),
      activation=None,
      use_bias=True,
      kernel_initializer=None,
      bias_initializer=tf.zeros_initializer(),
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      kernel_constraint=None,
      bias_constraint=None,
      trainable=True,
      name=None,
      reuse=None

  ) # convolution
• tf.layers.conv2d_transpose(

      inputs,
      filters,
      kernel_size,
      strides=(1, 1),
      padding='valid',
      data_format='channels_last',
      activation=None,
      use_bias=True,
      kernel_initializer=None,
      bias_initializer=tf.zeros_initializer(),
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      kernel_constraint=None,
      bias_constraint=None,
      trainable=True,
      name=None,
      reuse=None

  ) # upsample or deconvolution
• tf.add(x, y, name=None) # skip connection (element-wise addition)

Opimizers

• tf.train.AdamOptimizer(

      learning_rate=0.001,
      beta1=0.9,
      beta2=0.999,
      epsilon=1e-08,
      use_locking=False,
      name='Adam'

  )

Loss Function

• tf.nn.softmax_cross_entropy_with_logits(

      _sentinel=None,
      labels=None,
      logits=None,
      dim=-1,
      name=None

  )

Regularizer

• tf.contrib.layers.l2_regularizer(

      scale,
      scope=None

  )

Initializer 

• tf.random_normal_initializer(

      mean=0.0,
      stddev=1.0,
      seed=None,
      dtype=tf.float32

  )