lucifermorningstar1305

def convolutional_neural_network(x):
  weights = {'W_conv1' : tf.Variable(tf.random_normal([3,3,3,64])),
             'W_conv2' : tf.Variable(tf.random_normal([3,3,64,64])),
             'W_conv3' : tf.Variable(tf.random_normal([3,3,64,128])),
             'W_conv4' : tf.Variable(tf.random_normal([3,3,128,128])),
             'W_conv5': tf.Variable(tf.random_normal([3,3,128,256])),
             'W_conv6' : tf.Variable(tf.random_normal([3,3,256,256])),
             'W_conv7' : tf.Variable(tf.random_normal([3,3,256,256])),
             'W_conv8' : tf.Variable(tf.random_normal([3,3,256,512])),
             'W_conv9' : tf.Variable(tf.random_normal([3,3,512,512])),

lucifermorningstar1305

activation_function = lambda x: 1.0/(1.0 + np.exp(-x))
input = np.random.randn(3,1)
W1 = np.random.randn(None, 1)
W2 = np.random.randn(None,1)
W3 = np.random.randn(None,1)
b1 = np.zeros(1)
b2 = np.zeros(1)
b3 = np.zeros(1)
hidden_1 = activation(np.dot(W1, input) + b1)
hidden_2 = activation(np.dot(W2, W1) + b2)

lucifermorningstar1305

Mean_filter = np.array([[1,1,1], [1,1,1], [1,1,1]])/float(9)
Gm = scipy.signal.convolve2d(gray,Mean_filter,mode='same')
plt.imshow(Gm,cmap='gray')
plt.show()

lucifermorningstar1305

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
Gm = cv2.medianBlur(gray,3)

imgs = np.array([gray, Gm])
labels = ['Original','Filtered']
for i in range(1, column*row+1):
    ax = fig.add_subplot(row,column,i)
    ax.set_title(labels[i-1])
    plt.imshow(imgs[i-1], cmap='gray')
plt.show()

lucifermorningstar1305

Hg = np.zeros((20,20))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
for i in range(20):
    for j in range(20):
        Hg[i,j] = np.exp(-((i-10) ** 2 + (j-10)**2)/10)
        
gaussian_blur = scipy.signal.convolve2d(gray, Hg, mode='same')
gray_high = gray - gaussian_blur
gray_enhanced = gray + 0.025 * gray_high

lucifermorningstar1305

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
Hx = np.array([[1,0,-1], [2,0,-2],[1,0,-1]], dtype=np.float32)
Hy = np.array([[-1,-2,-1],[0,0,0],[1,2,1]], dtype=np.float32)
Gx = scipy.signal.convolve2d(Gm, Hx, mode ='same')
Gy = scipy.signal.convolve2d(Gm,Hy,mode = 'same')
G = (Gx*Gx + Gy*Gy) ** 0.5 

lucifermorningstar1305

lucifermorningstar1305

import numpy as np
from sklearn import preprocessing
features = np.array([[-500, 5], 
                    [2, 10],
                    [4, 100],
                    [9, 10]])
# Create the MinMax Scaler
minmax_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))
# Apply the MinMax Rescaling on our feature maps 
scaled_feature = minmax_scaler.fit_transform(features)

lucifermorningstar1305

import numpy as np
from sklearn import preprocessing
features = np.array([[-500, 5], 
                    [2, 10],
                    [4, 100],
                    [9, 10]])
# Create the Standard Scaler
standard_scaler = preprocessing.StandardScaler()
# Apply the Standard Rescaling on our feature maps 
scaled_feature = standard_scaler.fit_transform(features)

lucifermorningstar1305

> ]
> activate .
> add IJulia BenchmarkTools Plots DataFrames CSV MLJ MLJModels TextAnalysis PyCall Chain Pipe Compose Gadfly Query Statistics StatsBase StableRNGs PrettyPrinting