tensorflow convolutional neural network

An example of image recognition based on convolutional neural network is the preprocessing of input image in common use. Step1:resize STEP2: Go to mean value. It should be noted here that the average is calculated for all training sample images, and then the average is subtracted from each sample picture. The test picture is also subtracted from the mean when i

1Import TensorFlow asTF2Import NumPy asNP3Ts_norm=tf.random_normal ([ +])4With TF. Session () asSess:5Norm_data=Ts_norm.eval ()6Print (norm_data[:5])7Import Matplotlib.pyplot asPLT8 plt.hist (norm_data)9 plt.show ()Tendef layer_debug (output_dim,input_dim,inputs,activation=None): Onew=TF. Variable (Tf.random_normal ([Input_dim,output_dim])) AB=TF. Variable (Tf.random_normal ([1, Output_dim])) -Xwb=tf.matmul (INPUTS,W) +b - ifActivation isNone: the

(Layers_inputs[-1], 1, activation_function=none) cost = Tf.reduce_mean (Tf.reduce_sum (Tf.square (Ys-predi ction), reduction_indices=[1]) Train_op = Tf.train.GradientDescentOptimizer (0.001). Minimize (cost) return [Train_op, Cost, Layers_inputs] Fix_seed (1) x_data = Np.linspace ( -7, ten, 2500) [:, Np.newaxis] #水平轴 -7~10np.random.shuffle (x_data) No ise = np.random.normal (0, 8, x_data.shape) Y_data = Np.square (x_data)-5 + Noisexs = Tf.placeholder (Tf.float32, [None, 1]) # [Num_samples, N