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

)