Create quick_start.rst (PaddlePaddle#819)

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Author: acosta123

doc/fluid/beginners_guide/quick_start.rst

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+Quick Start
+=============
+
+Quick Installation
+--------------------
+
+PaddlePaddle supports quick installation by pip. Execute the following commands to finish quick installation of the CPU version:
+
+.. code-block:: bash
+
+	pip install paddlepaddle
+
+If you need to install the GPU version, or look up more specific installation methods, please refer to `Installation Instructions <../beginners_guide/install/index_en.html>`_
+
+
+Quick Usage
+-------------
+
+First, you need to import the fluid library
+
+.. code-block:: python
+
+	import paddle.fluid as fluid
+
+* Tensor Operations
+
+
+The following simple examples may help you quickly know about Fluid:
+
+1.use Fluid to create a one-dimensional array with five elements, and each element is 1
+
+.. code-block:: python
+    
+	# define the dimension of an array and the data type, and the parameter 'shape' can be modified to define an array of any size
+	data = fluid.layers.ones(shape=[5], dtype='int64')
+	# compute on the CPU
+	place = fluid.CPUPlace()
+	# create executors
+	exe = fluid.Executor(place)
+	# execute computation
+	ones_result = exe.run(fluid.default_main_program(),
+	                        # get data
+				fetch_list=[data], 
+				return_numpy=True)
+	# output the results
+	print(ones_result[0])
+
+you can get the results:
+
+.. code-block:: text
+
+	[1 1 1 1 1]
+
+2.use Fluid to add two arrays by bits
+
+.. code-block:: python
+
+	# call elementwise_op to add the generative arrays by bits
+	add = fluid.layers.elementwise_add(data,data)
+	# define computation place
+	place = fluid.CPUPlace()
+	exe = fluid.Executor(place)
+	# execute computation
+	add_result = exe.run(fluid.default_main_program(),
+	                 fetch_list=[add],
+	                 return_numpy=True)
+	# output the results
+	print (add_result[0])
+
+you can get the results:
+
+.. code-block:: text
+
+	[2 2 2 2 2]
+
+3.use Fluid to transform the data type
+
+.. code-block:: python
+
+	# transform a one-dimentional array of int to float64
+	cast = fluid.layers.cast(x=data, dtype='float64')
+	# define computation place to execute computation
+	place = fluid.CPUPlace()
+	exe = fluid.Executor(place)
+	cast_result = exe.run(fluid.default_main_program(),
+	                 fetch_list=[cast],
+	                 return_numpy=True)
+	# output the results
+	print(cast_result[0])
+
+you can get the results:
+
+.. code-block:: text
+
+	[1. 1. 1. 1. 1.]
+
+
+Operate the Linear Regression Model
+-------------------------------------
+
+By the simple example above, you may have known how to operate data with Fluid to some extent, so please try to create a test.py, and copy the following codes.
+
+This a a simple linear regression model to help us quickly solve the quaternary linear equation.
+
+.. code-block:: python
+
+	#load the library
+	import paddle.fluid as fluid
+	import numpy as np
+	#generate data
+	np.random.seed(0)
+	outputs = np.random.randint(5, size=(10, 4))
+	res = []
+	for i in range(10):
+		# assume the equation is y=4a+6b+7c+2d
+		y = 4*outputs[i][0]+6*outputs[i][1]+7*outputs[i][2]+2*outputs[i][3]
+		res.append([y])
+	# define data
+	train_data=np.array(outputs).astype('float32')
+	y_true = np.array(res).astype('float32')
+
+	#define the network
+	x = fluid.layers.data(name="x",shape=[4],dtype='float32')
+	y = fluid.layers.data(name="y",shape=[1],dtype='float32')
+	y_predict = fluid.layers.fc(input=x,size=1,act=None)
+	#define loss function
+	cost = fluid.layers.square_error_cost(input=y_predict,label=y)
+	avg_cost = fluid.layers.mean(cost)
+	#define optimization methods
+	sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.05)
+	sgd_optimizer.minimize(avg_cost)
+	#initialize parameters
+	cpu = fluid.CPUPlace()
+	exe = fluid.Executor(cpu)
+	exe.run(fluid.default_startup_program())
+	##start training and iterate for 500 times
+	for i in range(500):
+		outs = exe.run(
+			feed={'x':train_data,'y':y_true},
+			fetch_list=[y_predict.name,avg_cost.name])
+		if i%50==0:
+			print ('iter={:.0f},cost={}'.format(i,outs[1][0]))
+	#save the training result
+	params_dirname = "result"
+	fluid.io.save_inference_model(params_dirname, ['x'], [y_predict], exe)
+
+	# start inference
+	infer_exe = fluid.Executor(cpu)
+	inference_scope = fluid.Scope()
+	# load the trained model
+	with fluid.scope_guard(inference_scope):
+		[inference_program, feed_target_names,
+		 fetch_targets] = fluid.io.load_inference_model(params_dirname, infer_exe)
+
+	# generate test data
+	test = np.array([[[9],[5],[2],[10]]]).astype('float32')
+	# inference
+	results = infer_exe.run(inference_program,
+							feed={"x": test},
+							fetch_list=fetch_targets) 
+	# give the problem 【9,5,2,10】 and output the value of y=4*9+6*5+7*2+10*2
+	print ("9a+5b+2c+10d={}".format(results[0][0]))
+
+.. code-block:: text
+
+    get the result:
+	
+	9a+5b+2c+10d=[99.946]
+	
+The output result should be a value close to 100, which may have a few errors every time.
+	
+    
+	
+