Build basic model in Tensorflow

Tensorflow separates definition of computations from their execution

Phase 1: assemble a graph.

• Step 1: Read in data

 # data input script

• Step 2: Create placeholders for inputs and labels

 tf.placeholder(dtype, shape=None, name=None)

• Step 3:Create weight an bias

  tf.Variable(initial_value=None, trainable=True, collections=None, name=None, dtype=None,...)

• Step 4: Build model to predict Y

  # tensorflow operation

• Step 5:Specify loss function

tf.nn.softmax_cross_entropy_with_logits()

• Step 6: Create optimizer

tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

Phase 2: ues a session to execute operations in the graph.

• Step 1: Initialize variables

  tf.global_variables_initializer()
  tf.summary.FileWriter('./graphs', sess.graph)

• Step 2: Run optimizer op(with data fed into placeholders for inputs and labels)

  sess.run(op, feed_dict={X:X_batch, Y:Y_batch})

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