TensorFlow™ is an open-source software library created by Google for numerical computation and large scale computation. Here is the Tensor Flow Cheat Sheet for you!
pip install <package-name>
Example
pip install requests
sudo pip install
https://storage.googleapis.com/tensorflow/linux/$device/tensorflow-0.8.0-$python_version-none-linux_x86_64.whl
sudo pip install
pip install sklearn
pip install keras
Python helper Important functions
type(object) | Get object type |
help(object) | Get help for object (list of available methods, attributes, signatures and so on) |
dir(object) | Get list of object attributes (fields, functions) |
str(object) | Transform an object to string object? Shows documentations about the object |
globals() | Return the dictionary containing the current scope’s global variables. |
locals() | Update and return a dictionary containing the current scope’s local variables. |
id(object) | Return the identity of an object. This is guaranteed to be unique among simultaneously existing objects. |
import builtindir(builtin) | Other built-in functions |
| TensorFlowClassifier | |
| TensorFlowRegressor | |
| TensorFlowDNNClassifier | |
| TensorFlowDNNRegressor | |
| TensorFlowLinearClassififier | |
| TensorFlowLinearRegressor | |
| TensorFlowRNNClassifier | |
| TensorFlowRNNRegressor | |
| TensorFlowEstimator |
Each classifier and regressor have following fields n_classes=0 (Regressor), n_classes are expected to be input (Classifier)
batch_size=32, steps=200, // except TensorFlowRNNClassifier - there is 50 optimizer=’Adagrad’, learning_rate=0.1,
tf.Graph() | A TensorFlow computation, represented as a dataflow graph. |
tf.Operation() | Represents a graph node that performs computation on tensors. |
tf.Tensor() | A tensor represents a rectangular array of data. |
tf.get_default_graph() | Returns the default graph for the current thread. |
tf.reset_default_graph() | Returns the default graph for the current thread. |
tf.device(“/cpu:0”) | |
tf.name_scope(value) | A context manager for use when defining a Python op. |
tf.convert_to_tensor(value) | Converts the given value to a Tensor. |
ops.reset_default_graph() | Clears the default graph stack and resets the global default graph. |
GradientDescentOptimizer | Optimizer that implements the gradient descent algorithm. |
AdadeltaOptimizer | Optimizer that implements the Adadelta algorithm. |
AdagradOptimizer | Optimizer that implements the Adagrad algorithm. |
MomentumOptimizer | Optimizer that implements the Momentum algorithm. |
AdamOptimizer | Optimizer that implements the Adam algorithm. |
FtrlOptimizer | Optimizer that implements the FTRL algorithm. |
RMSPropOptimizer | Optimizer that implements the RMSProp algorithm |
reduce_sum | Computes the sum of elements across dimensions of a tensor. |
reduce_prod | Computes the product of elements across dimensions of a tensor. |
reduce_min | Computes the minimum of elements across dimensions of a tensor. |
reduce_max | Computes the maximum of elements across dimensions of a tensor. |
reduce_mean | Computes the mean of elements across dimensions of a tensor. |
reduce_any | Computes the “logical or” of elements across dimensions of a tensor. |
accumulate_n | Returns the element-wise sum of a list of tensors. |
tf.nn? | Wrappers for primitive Neural Net (NN) Operations. |
relu | Computes rectified linear: max(features, 0). |
relu6 | Computes Rectified Linear 6: min(max(features, 0), 6). |
elu | Computes exponential linear: exp(features) - 1 if < 0, features otherwise. |
softplus | Computes softplus: log(exp(features) + 1). |
softsign | Computes softsign: features / (abs(features) + 1). |
dropout | Applies Dropout to the input. |
bias_add | Adds bias to value. |
sigmoid | Computes sigmoid of x element-wise. |
tanh | Computes hyperbolic tangent of x element-wise. |
sigmoid_cross_entropy_with_logits | Computes sigmoid cross entropy given logits. |
softmax | Computes softmax activations. |
log_softmax | Computes log softmax activations. |
softmax_cross_entropy_with_logits | Computes softmax cross entropy between logits and labels. |
sparse_softmax_cross_entropy_with_logits | Computes sparse softmax cross entropy between logits and labels. |
weighted_cross_entropy_with_logits | Computes a weighted cross entropy. |