# Le-Net 1 based architecture

We start with 41X41 (I) after first convolution (9x9)we have 33X33 (L1). The next pooling layer reduces dimension with 3 to an output image of 11X11 with 4x4 pooling kernels (L2). Then we apply different types of convolution 4x4 kernels on the L2 layer resulting in 8x8 (L3) . Then followed by pooling 2X2 resulting in 4x4 output map (L4). So we have 16 connection for each element in layer L4 (which depend on the amount of different Covolutions in L3)

\begin{equation}
f(x)=\frac{1}{1+e^{-x}} \\
F_{k}= f( \sum_{i} \mathbf{W^{k}_{i} \cdot y_{i}}-b_{k})
\end{equation}

\begin{equation}
E=\sum_{k} \frac{1}{2}|t_k-F_{k}|^{2} \\
\Delta W_{ij}= - \eta \frac{dE}{d W_{ij}}
\end{equation}

\begin{equation}
\Delta W_{ij}= \sum_{k} - \eta \frac{dE}{d F_{k}} \frac{dF_{k}}{dx_{k}} \frac{dx_{k}}{dW_{ij}}=\sum_{k} \eta (t_{k}-F_{k})\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}} \frac{dx_{k}}{dW_{ij}} \\
= \eta (t_{k}-F_{k})\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}} y_{ij}
\end{equation}

\begin{equation}
\Delta b_{k}= - \eta \frac{dE}{d F_{k}} \frac{dF_{k}}{dx_{k}} \frac{dx_{k}}{b_{k}}=\eta (t_{k}-F_{k})\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}} \cdot-1
\end{equation}

Since $\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}}$ is always positive we can neglect this term in our programme

\begin{equation}
x_{k}=\sum_{ij} W^{k}[i,j] \; y^{4rb}[i,j] - b_{k}
\end{equation}

\begin{equation}
y^{4rb}[i,j]= \sum_{u,v} W^{3rb}[u,v] \; y^{3rb} [2i+u,2j+v]
\end{equation}

\begin{equation}
y^{3rb} [2i+u,2j+v]= f\left (x^{3rb}[2i+u,2j+v] \right)
\end{equation}

\begin{equation}
x^{3rb}[2i+u,2j+v]=\sum_{nm} W^{2rb}[n,m] \; y^{2rb}[n+(2i+u),m+(2j+v)] -b^{3rb}[2i+u,2j+v]
\end{equation}

\begin{equation}
\begin{split}
\Delta W^{2rb}[n,m] =\sum_{k} - \eta  \frac{dE}{dF_{k}}
\frac{dF_{k}}{dx_{k}}
\sum_{ij} \frac{dx_{k}}{dy^{4rb}[i,j]}
 \sum_{uv}\frac{dy^{4rb}[i,j]}{d y^{3rb} [2i+u,2j+v]}
\frac{d y^{3rb} [2i+u,2j+v]}{d x^{3rb}[2i+u,2j+v]}
\sum_{nm}\frac{d x^{3rb}[2i+u,2j+v]}{d W^{2rb}[n,m]}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
\Delta b^{3rb}[2i+u,2j+v] =\sum_{k} - \eta  \frac{dE}{dF_{k}}
\frac{dF_{k}}{dx_{k}}
\sum_{ij} \frac{dx_{k}}{dy^{4rb}[i,j]}
 \sum_{uv}\frac{dy^{4rb}[i,j]}{d y^{3rb} [2i+u,2j+v]}
\frac{d y^{3rb} [2i+u,2j+v]}{d x^{3rb}[2i+u,2j+v]}
\frac{d x^{3rb}[2i+u,2j+v]}{d b^{3rb}[2i+u,2j+v]}
\end{split}
\end{equation}

\begin{equation}
    \frac{dx_{k}}{dy^{4rb}[i,j]} = W^{4rbk}[i,j]\\
\end{equation}

\begin{equation}
    \frac{dy^{4rb}[i,j]}{d y^{3rb} [2i+u,2j+v]} = W^{3rb}[u,v] \\
 \end{equation}

\begin{equation}
\frac{d y^{3rb} [2i+u,2j+v]}{d x^{3rb}[2i+u,2j+v]}=\frac{e^{-x^{3rb}[2i+u,2j+v]}}{(1+e^{-x^{3rb}[2i+u,2j+v]})^2}
\end{equation}
This term is first not included since it is always positive. If the training will not converge it might be possible to include this term

 \begin{equation}
    \frac{d y^{3rb} [2i+u,2j+v]}{d W^{2rb}[n,m]}= y^{2rb} [n+(2i+u),m+(2j+v)] \\
\end{equation}

\begin{equation}
\frac{d x^{3rb}[2i+u,2j+v]}{d b^{3rb}[2i+u,2j+v]}=-1
\end{equation}

```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from numpy import linalg as lin
import scipy.signal as sig
from PIL import Image
import glob
import matplotlib.cm as cm
import itertools
```

```python
########### Load Input ############################################################################################################################
# In this script I used the brightness to determine structures, instead of one RGB color:
# this is determined by: 0.2126*R + 0.7152*G + 0.0722*B
# Source: https://en.wikipedia.org/wiki/Relative_luminance

patchSize=40 # patchsize this must be 48 since our network can only handle this value

# Open forest
Amount_data= len(glob.glob('Forest/F*'))
Patches_F=np.empty([1,patchSize,patchSize])
Patches_F_RGB=np.empty([1,patchSize,patchSize,3])
Patches_t=np.empty([3])

for k in range (0, Amount_data):
    name="Forest/F%d.png" % (k+1)
    img = Image.open(name)
    data=img.convert('RGB')
    data= np.asarray( data, dtype="int32" )
    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]
    data2=img.convert('RGB')
    data2= np.asarray( data2, dtype="int32" )

    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture

    # Create patches for structure
    data_t=np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)])
    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize])
    Patches_F=np.append(Patches_F,data_t,axis=0)
    #Create patches for colour
    data_t=np.array([[data2[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize,:] for i in range(0,Xamount)] for j in range(0,Yamount)])
    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize, 3])
    Patches_F_RGB=np.append(Patches_F_RGB, data_t,axis=0)
Patches_F=np.delete(Patches_F, 0,0)
Patches_F_RGB=np.delete(Patches_F_RGB, 0,0)

# Open city
Amount_data= len(glob.glob('City/C*'))
Patches_C=np.empty([1,patchSize,patchSize])
Patches_C_RGB=np.empty([1,patchSize,patchSize,3])
Patches_t=np.empty([3])

for k in range (0, Amount_data):
    name="City/C%d.png" % (k+1)
    img = Image.open(name)
    data=img.convert('RGB')
    data = np.asarray( data, dtype="int32" )
    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]
    data2=img.convert('RGB')
    data2= np.asarray( data2, dtype="int32" )

    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture

    # Create patches for structure
    data_t=np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)])
    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize])
    Patches_C=np.append(Patches_C,data_t,axis=0)
    #Create patches for colour
    data_t=np.array([[data2[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize,:] for i in range(0,Xamount)] for j in range(0,Yamount)])
    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize, 3])
    Patches_C_RGB=np.append(Patches_C_RGB, data_t,axis=0)

Patches_C=np.delete(Patches_C, 0,0)
Patches_C_RGB=np.delete(Patches_C_RGB, 0,0)

# Open water
Amount_data= len(glob.glob('Water/W*'))
Patches_W=np.empty([1,patchSize,patchSize])
Patches_W_RGB=np.empty([1,patchSize,patchSize,3])
Patches_t=np.empty([3])

for k in range (0, Amount_data):
    name="Water/W%d.png" % (k+1)
    img = Image.open(name)
    data=img.convert('RGB')
    data = np.asarray( data, dtype="int32" )
    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]

    data2 = img.convert('RGB')
    data2 = np.asarray( data2, dtype="int32" )

    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture

    # Create patches for structure
    data_t=np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)])
    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize])
    Patches_W=np.append(Patches_W,data_t,axis=0)
    #Create patches for colour
    data_t=np.array([[data2[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize,:] for i in range(0,Xamount)] for j in range(0,Yamount)])
    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize, 3])
    Patches_W_RGB=np.append(Patches_W_RGB, data_t,axis=0)

Patches_W=np.delete(Patches_W, 0,0)
Patches_W_RGB=np.delete(Patches_W_RGB, 0,0)

# Open Grassland
#Amount_data= len(glob.glob('Grassland/G*'))
#Patches_G=np.empty([1,patchSize,patchSize])
#Patches_G_RGB=np.empty([1,patchSize,patchSize,3])
#Patches_t=np.empty([3])

#for k in range (0, Amount_data):
#    name="Grassland/G%d.png" % (k+1)
#    img = Image.open(name)
#    data=img.convert('RGB')
#    data= np.asarray( data, dtype="int32" )
#    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]
#    data2=img.convert('RGB')
#    data2= np.asarray( data2, dtype="int32" )

#    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
#    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture

    # Create patches for structure
#    data_t=np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)])
#    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize])
#    Patches_G=np.append(Patches_G,data_t,axis=0)
    #Create patches for colour
#    data_t=np.array([[data2[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize,:] for i in range(0,Xamount)] for j in range(0,Yamount)])
#    data_t=np.reshape(data_t, [data_t.shape[0]*data_t.shape[1], patchSize, patchSize, 3])
#    Patches_G_RGB=np.append(Patches_G_RGB, data_t,axis=0)
#Patches_G=np.delete(Patches_G, 0,0)
#Patches_G_RGB=np.delete(Patches_G_RGB, 0,0)

```

```python
print Patches_C.shape[0], Patches_W.shape[0], Patches_F.shape[0] #, Patches_G.shape[0]
```

    7480 7972 7163

```python
########### Functions ############################################################################################################################

# Define Activitation functions, pooling and convolution functions (the rules)

def Sigmoid(x):
    return (1/(1+np.exp(-x)))

def Sigmoid_dx(x):
    return np.exp(-x)/((1+np.exp(-x))**2)

def TanH(x):
    return (1-np.exp(-x))/(1+np.exp(-x))

def Pool(I,W):
    PoolImg=np.zeros((len(I)/len(W),len(I)/len(W))) # W must fit an integer times into I.
    for i in range(0,len(PoolImg)):
        for j in range(0,len(PoolImg)):
            SelAr=I[i*len(W):(i+1)*len(W),j*len(W):(j+1)*len(W)]
            PoolImg[i,j]=np.inner(SelAr.flatten(),W.flatten()) # Now this is just an inner product since we have vectors
    return PoolImg

# To automatically make Gaussian kernels
def makeGaussian(size, fwhm = 3, center=None):
    x = np.arange(0, size, 1, float)
    y = x[:,np.newaxis]

    if center is None:
        x0 = y0 = size // 2
    else:
        x0 = center[0]
        y0 = center[1]

    return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)

# To automatically define pooling nodes
def Pool_node(N):
    s=(N,N)
    a=float(N)*float(N)
    return (1.0/a)*np.ones(s)

```

```python
#################### Define pooling layers ###########################################################################
P12=Pool_node(4)*(1.0/100.0) #factor 1000 added to lower values more
P34=Pool_node(1)*(1.0/10.0)

#################### Define Convolution layers #######################################################################

######### First C layer #########
C1=[]

## First Kernel

# Inspiration: http://en.wikipedia.org/wiki/Sobel_operator
# http://stackoverflow.com/questions/9567882/sobel-filter-kernel-of-large-size

Kernel=np.array([[4,3,2,1,0,-1,-2,-3,-4],
                 [5,4,3,2,0,-2,-3,-4,-5],
                 [6,5,4,3,0,-3,-4,-5,-6],
                 [7,6,5,4,0,-4,-5,-6,-7],
                 [8,7,6,5,0,-5,-6,-7,-8],
                 [7,6,5,4,0,-4,-5,-6,-7],
                 [6,5,4,3,0,-3,-4,-5,-6],
                 [5,4,3,2,0,-2,-3,-4,-5],
                 [4,3,2,1,0,-1,-2,-3,-4]])

C1.append(Kernel)

## Second Kernel
Kernel=np.matrix.transpose(Kernel)
C1.append(Kernel)

##Third Kernel
#Kernel=makeGaussian(9,5)
#Kernel=(1/np.sum(Kernel))*Kernel
#C1.append(Kernel)

######### Initialize output weights and biases #########

# Define the number of branches in one row
N_branches= 3
ClassAmount=3 # Forest, City, Water, Grassland
Size_C2=5
S_H3=((patchSize-C1[0].shape[0]+1)/P12.shape[1])-Size_C2+1
S_H4=S_H3/P34.shape[1]

C2_INIT=np.random.rand(len(C1),N_branches, Size_C2, Size_C2) # second convolution weigths
W_INIT=np.random.rand(ClassAmount, len(C1), N_branches, S_H3, S_H3) # end-weight from output to classifier-neurons
W2_INIT=np.random.rand(ClassAmount,3)
H3_bias=np.random.rand(len(C1),N_branches) # bias in activation function from C2 to H3
Output_bias=np.random.rand(ClassAmount) # bias on the three classes

```

```python
N_plts=len(C1)
for i in range(0,N_plts):
    plt.subplot(4,3,i+1)
    plt.imshow(C1[i])
```

# For the extra information regarding the code in the following cell

a random patch is chosen in the following way: the program counts how many files and patches there are in total, then it permutes the sequence so that a random patch is chosen every iteration (forest, city, water). After selecting the number the file has to be found back.

```python
N_F=Patches_F.shape[0]
N_C=Patches_C.shape[0]
N_W=Patches_W.shape[0]
#N_G=Patches_G.shape[0]

N_total=N_F+N_C+N_W#+N_G
Sequence = np.arange(N_total)
Sequence = np.random.permutation(Sequence)
```

```python
print N_F, N_C, N_W#, N_G
print N_total
```

    7163 7480 7972
    22615

```python
# TRAINING PHASE: WITH COLOUR
#delta_H4=np.zeros((len(C1), N_branches, S_H4, S_H4))
#delta_H3=np.zeros((len(C1), N_branches, S_H4, S_H4))
C2=C2_INIT
W=W_INIT
W2=W2_INIT

n_W=1
n_C2=1.5*10**-2

Sample_iterations=0
N_1000=0

from itertools import product
###### Chooses patch and defines label #####
#for PP in range(0,len(Sequence)):
for PP in range(0,6000):
    SS=Sequence[PP]
    #SS=14000
    if SS<N_F:
        Class_label=np.array([1,0,0])
        inputPatch=Patches_F[SS]
        Int_RGB=np.mean(np.mean(Patches_F_RGB[SS,:,:,:], axis=0), axis=0)/255

    elif(SS>=N_F) and (SS<(N_F+N_C)):
        Class_label=np.array([0,1,0])
        inputPatch=Patches_C[SS-N_F]
        Int_RGB=np.mean(np.mean(Patches_C_RGB[SS-N_F,:,:,:], axis=0), axis=0)/255

    elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
        Class_label=np.array([0,0,1])
        inputPatch=Patches_W[SS-N_F-N_C]
        Int_RGB=np.mean(np.mean(Patches_W_RGB[SS-N_F-N_C,:,:,:], axis=0), axis=0)/255

    #else:
    #    Class_label=np.array([0,0,0,1])
    #    inputPatch=Patches_G[SS-N_F-N_C-N_W]
    #    Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
    H1=[]
    H2=[]
    H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
    H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
    x=np.zeros(ClassAmount)
    f=np.zeros(ClassAmount)

    II=1
    ITER=0
    while II==1:

        for r in range (0, len(C1)):
            H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
            H2.append(Pool(H1[r], P12))
    #From here on BP trakes place!
            for b in range(0,N_branches):
                H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                H4[r][b]=Pool(H3[r][b],P34)
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
        y=np.append([H4.flatten()], [Int_RGB])
        for k in range(0,ClassAmount):
            W_t=np.append([W[k].flatten()], [W2[k]])
            x[k]=np.inner(y, W_t)
            f[k]=Sigmoid(x[k]-Output_bias[k])
        #f=f/np.sum((f))

        ###### Back-propagation #####
        # First learning the delta's
        delta_H4=np.zeros([ClassAmount,len(C1),N_branches,S_H4,S_H4])
        e_k=f-Class_label
        delta_k=e_k*Sigmoid_dx(x)
        #Output_bias=Output_bias[k]+n_bias*e_k
        for k in range(0, ClassAmount):
            #update weights output layer
            W[k]=W[k]-n_W*delta_k[k]*H4
            W2[k]=W2[k]-n_W*delta_k[k]*Int_RGB
            delta_H4[i]=delta_k[i]*W[i]
        delta_H4=np.sum(delta_H4, axis=0)
        delta_H3=(float(1)/10)*delta_H4
        C2_diff=np.zeros([len(C1),N_branches, Size_C2, Size_C2])
        for r in range(0, len(C1)):
            C2_t=np.array([[delta_H3[r][:]*H2[r][(0+u):(4+u),(0+v):(4+v)] for u in range(0,Size_C2)] for v in range (0,Size_C2)])
            C2_t=np.sum(np.sum(C2_t, axis=4),axis=3)
            C2_t=np.rollaxis(C2_t,2)
            C2_diff[r]=-n_C2*C2_t
        C2=C2+C2_diff
        ERROR=np.sum((Class_label-f)**2)
        ITER=ITER+1

        if ERROR<0.55 or ITER>4:
            II=0

    Sample_iterations=Sample_iterations+1
    if Sample_iterations>1000:
        n_W=0.7
        n_C2=0.7*1.5*10**-2
    if Sample_iterations>2000:
        n_W=0.7*0.7
        n_C2=0.7*0.7*1.5*10**-2

    if Sample_iterations>3000:
        n_W=0.7*0.7*0.7
        n_C2=0.7*0.7*0.7*1.5*10**-2

    if Sample_iterations>5000:
        n_W=0.2
        n_C2=0.0025

    if Sample_iterations>7500:
        n_W=0.1
        n_C2=0.001

    if Sample_iterations>10000:
        n_W=0.01
        n_C2=0.0005

    print f, np.argmax(Class_label)
    if np.argmax(f)==np.argmax(Class_label):
        print True
    else:
        print False

#    Sample_iterations=Sample_iterations+1
    if (Sample_iterations-(1000*N_1000))==1000:
        print Sample_iterations
        N_1000=N_1000+1
#        n_W=0.7*n_W
#      n_C2=0.7*n_C2
```

    [ 0.39361419  0.98184301  0.23449627] 1
    True
    [ 0.79045424  0.13992539  0.68416107] 0
    True
    [ 0.4549695   0.83344204  0.29403904] 0
    False
    [ 0.51065314  0.48795303  0.35689138] 0
    True
    [ 0.76903177  0.22003828  0.43187943] 0
    True
    [ 0.68237177  0.43030057  0.39829358] 0
    True
    [ 0.81147348  0.17320406  0.51455586] 0
    True
    [ 0.53735173  0.35723227  0.65967675] 2
    True
    [ 0.36248522  0.40660308  0.60908073] 2
    True
    [ 0.55037661  0.69836753  0.17117274] 1
    True
    [ 0.5300573   0.64907886  0.32507719] 1
    True
    [ 0.44247266  0.54818936  0.40668967] 1
    True
    [ 0.70582743  0.32617565  0.29193188] 0
    True
    [ 0.52148717  0.12948147  0.59057501] 2
    True
    [ 0.75395374  0.1311756   0.46353186] 0
    True
    [ 0.43109056  0.72466298  0.20171992] 1
    True
    [ 0.1552415   0.57841714  0.58120763] 2
    True
    [ 0.46093874  0.51691096  0.23782308] 1
    True
    [ 0.26943473  0.85180758  0.12578948] 1
    True
    [ 0.29475649  0.13401007  0.72087975] 2
    True
    [ 0.70790904  0.13857155  0.32673628] 0
    True
    [ 0.25927595  0.77151326  0.27352516] 1
    True
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    [ 0.36753463  0.29523644  0.31599047] 1
    False
    [ 0.51060402  0.05017629  0.62199563] 2
    True
    [ 0.28239146  0.34831821  0.39988144] 2
    True
    [ 0.34573427  0.20222227  0.3948941 ] 2
    True
    [ 0.2786255   0.50889856  0.28215321] 1
    True
    [ 0.4454228   0.14612284  0.43623085] 2
    False
    [ 0.43005201  0.08371556  0.59721728] 2
    True
    [ 0.4202442   0.22171278  0.40336831] 0
    True
    [ 0.39088793  0.13299804  0.48666026] 0
    False
    [ 0.42231066  0.17301454  0.42473509] 2
    True
    [ 0.48624714  0.16653038  0.33235141] 0
    True
    [ 0.59847202  0.06236956  0.50027772] 0
    True
    [ 0.46295404  0.18753577  0.34999224] 2
    False
    [ 0.57639608  0.10803702  0.36773297] 0
    True
    [ 0.42098139  0.14559349  0.49396471] 2
    True
    [ 0.31246273  0.51475942  0.23010822] 1
    True
    [ 0.27918345  0.56946664  0.23317707] 1
    True
    [ 0.17557937  0.82319379  0.14831788] 2
    False
    [ 0.38806195  0.12238775  0.54417281] 0
    False
    [ 0.41296493  0.12474691  0.46697351] 0
    False
    [ 0.27856949  0.6593676   0.16594081] 1
    True
    [ 0.210874    0.53886428  0.2890661 ] 1
    True
    [ 0.26027982  0.31519988  0.47543038] 2
    True
    [ 0.42406405  0.15169213  0.41379437] 2
    False
    [ 0.42654888  0.23945715  0.36815881] 0
    True
    [ 0.18023999  0.55720437  0.30850272] 1
    True
    [ 0.486525    0.24450057  0.29650873] 0
    True
    [ 0.42672374  0.16597728  0.44157817] 2
    True
    [ 0.50174566  0.04347318  0.63558245] 2
    True
    [ 0.35891523  0.51391035  0.20289897] 1
    True
    [ 0.53393558  0.01552851  0.79846389] 2
    True
    [ 0.22132805  0.83118585  0.10689196] 1
    True
    [ 0.48000632  0.1359193   0.41660688] 2
    False
    [ 0.40267436  0.08427596  0.58664027] 2
    True
    [ 0.31568114  0.26812461  0.42147587] 2
    True
    [ 0.38148868  0.21332237  0.40722399] 2
    True
    [ 0.31528728  0.41939283  0.28501009] 0
    False
    [ 0.49154075  0.12423606  0.41868796] 0
    True
    [ 0.30329484  0.59725422  0.19065178] 1
    True
    [ 0.29334208  0.65301417  0.17049118] 1
    True
    [ 0.39992173  0.32634397  0.28936637] 1
    False
    [ 0.1726678   0.60095953  0.25836449] 1
    True
    [ 0.39804067  0.3295194   0.297117  ] 1
    False
    [ 0.18006191  0.5769366   0.25919107] 1
    True
    [ 0.39604403  0.08326927  0.58943265] 2
    True
    [ 0.35754294  0.31732013  0.32087106] 0
    True
    [ 0.21381104  0.53038861  0.2593014 ] 1
    True
    [ 0.43053125  0.16962836  0.39958735] 0
    True
    [ 0.17829721  0.1983013   0.58311061] 2
    True
    [ 0.50570156  0.10808125  0.421084  ] 0
    True
    [ 0.35375848  0.21033222  0.43256698] 0
    False
    [ 0.49790704  0.09026482  0.4983149 ] 0
    False
    [ 0.43194841  0.240874    0.3317083 ] 2
    False
    [ 0.3968066   0.2466108   0.35857442] 0
    True
    [ 0.19579024  0.12125742  0.7006191 ] 2
    True
    [ 0.17556717  0.74810389  0.17695855] 1
    True
    [ 0.16190558  0.31486957  0.50600582] 2
    True
    [ 0.36908953  0.36855948  0.2627907 ] 1
    False
    [ 0.33082822  0.25839259  0.39891709] 2
    True
    [ 0.25678612  0.69678971  0.16491159] 1
    True
    [ 0.12142895  0.81088732  0.20874889] 1
    True
    [ 0.36065867  0.12272553  0.53307655] 2
    True
    [ 0.35358313  0.27382782  0.33735873] 0
    True
    [ 0.38389915  0.44302658  0.21056981] 1
    True
    [ 0.17717169  0.77057618  0.1684808 ] 1
    True
    [ 0.49704294  0.13764853  0.42222485] 0
    True
    [ 0.40895573  0.1559496   0.41850498] 0
    False
    [ 0.52078464  0.08368822  0.48166695] 2
    False
    [ 0.36571977  0.37593751  0.23836477] 1
    True
    [ 0.25777207  0.38973164  0.32418901] 1
    True
    [ 0.4265946   0.30513998  0.27927283] 1
    False
    [ 0.18752317  0.55411511  0.2824619 ] 1
    True
    [ 0.2390312   0.83438233  0.08608393] 1
    True
    [ 0.40480104  0.34923074  0.26512307] 0
    True
    [ 0.39056298  0.25420583  0.33621375] 0
    True
    [ 0.20954715  0.57590829  0.22108736] 1
    True
    [ 0.37450763  0.407714    0.23142452] 1
    True
    [ 0.34002355  0.44087707  0.23092396] 1
    True
    [ 0.3944276   0.20687506  0.3612803 ] 0
    True
    [ 0.19144278  0.72416599  0.16621211] 1
    True
    [ 0.3118092   0.5005039   0.24414241] 0
    False
    [ 0.26737744  0.50377216  0.27797899] 2
    False
    [ 0.28006828  0.38075     0.3297949 ] 2
    False
    [ 0.40812849  0.12832661  0.47349342] 0
    False
    [ 0.48791697  0.12587637  0.41904629] 0
    True
    [ 0.11554598  0.94669962  0.07432005] 1
    True
    [ 0.24211583  0.50965971  0.24762302] 1
    True
    [ 0.32416849  0.17727557  0.484595  ] 2
    True
    [ 0.20597867  0.6473474   0.2245641 ] 1
    True
    [ 0.39243067  0.22350049  0.34900584] 0
    True
    [ 0.42319903  0.17541352  0.41444553] 0
    True
    [ 0.116841    0.90442816  0.11856304] 1
    True
    [ 0.34651551  0.27185091  0.34565965] 0
    True
    [ 0.23832379  0.56758762  0.22419879] 2
    False
    [ 0.2343914   0.67050354  0.17675308] 1
    True
    [ 0.23727581  0.52429518  0.23869764] 1
    True
    [ 0.37702192  0.49890612  0.20569084] 1
    True
    [ 0.20048897  0.51317648  0.29130564] 1
    True
    [ 0.63111196  0.07553093  0.40173272] 0
    True
    [ 0.26260554  0.51593846  0.22856515] 1
    True
    [ 0.45554454  0.2511846   0.28425537] 1
    False
    [ 0.26045964  0.77108448  0.10629272] 1
    True
    [ 0.26825878  0.71489983  0.12880839] 1
    True
    [ 0.20983729  0.69467094  0.19028028] 1
    True
    [ 0.16522895  0.68981557  0.24167652] 1
    True
    [ 0.18730114  0.83449175  0.11874072] 1
    True
    [ 0.39702021  0.26779732  0.28324247] 1
    False
    [ 0.26257773  0.45883181  0.31008763] 2
    False
    [ 0.40946346  0.13343814  0.4219278 ] 0
    False
    [ 0.4458767   0.16644407  0.37811318] 0
    True
    [ 0.47406823  0.1054686   0.46568612] 0
    True
    [ 0.1221086   0.33356148  0.52638891] 2
    True
    [ 0.29751416  0.30856163  0.38022325] 2
    True
    [ 0.45089282  0.18744041  0.35572751] 0
    True
    [ 0.33333534  0.38764958  0.25910084] 0
    False
    [ 0.31793921  0.6178461   0.14891192] 1
    True
    [ 0.45743871  0.18964334  0.32394376] 0
    True
    [ 0.46454805  0.13779696  0.41147697] 2
    False
    [ 0.47822248  0.04575765  0.6377554 ] 2
    True
    [ 0.47115037  0.06459733  0.59703663] 2
    True
    [ 0.42819279  0.20642921  0.33890219] 0
    True
    [ 0.38525267  0.10346336  0.54931655] 2
    True
    [ 0.51267717  0.17251702  0.31319574] 0
    True
    [ 0.17216715  0.68358883  0.21912561] 1
    True
    [ 0.14238899  0.88108385  0.11764793] 1
    True
    [ 0.37875177  0.25671501  0.35895628] 2
    False
    [ 0.34233194  0.39800044  0.26253745] 0
    False
    [ 0.40210401  0.32336797  0.28433421] 1
    False
    [ 0.39869873  0.34549674  0.27323508] 0
    True
    [ 0.28677278  0.2832443   0.40130366] 2
    True
    [ 0.40728535  0.23599764  0.3664961 ] 2
    False
    [ 0.23414021  0.47531498  0.30167609] 1
    True
    [ 0.29255223  0.50596717  0.24220521] 1
    True
    [ 0.4410941   0.14984718  0.42717217] 2
    False
    [ 0.16501497  0.2133936   0.60718822] 2
    True
    [ 0.32685585  0.53069088  0.21708156] 1
    True
    [ 0.32027722  0.22483889  0.4954392 ] 2
    True
    [ 0.46975382  0.15410666  0.35567294] 0
    True
    [ 0.18107568  0.84174985  0.13070355] 1
    True
    [ 0.15360425  0.71590365  0.22605041] 1
    True
    [ 0.3459478   0.41379348  0.23181401] 0
    False
    [ 0.36908506  0.40804504  0.24351724] 1
    True
    [ 0.34800807  0.3927447   0.27418559] 0
    False
    [ 0.2295655   0.46067156  0.29260648] 1
    True
    [ 0.21585504  0.4512507   0.33116749] 1
    True
    [ 0.423075    0.07437571  0.59552113] 2
    True
    [ 0.41419232  0.20640171  0.37107982] 0
    True
    [ 0.19934473  0.12709751  0.69228193] 2
    True
    [ 0.18515446  0.761694    0.15465803] 2
    False
    [ 0.30737577  0.67389303  0.14271291] 1
    True
    [ 0.31696661  0.60046411  0.14685018] 1
    True
    [ 0.48421164  0.1561046   0.36678177] 0
    True
    [ 0.46572651  0.37375122  0.17883723] 1
    False
    [ 0.16770829  0.74550914  0.18747674] 1
    True
    [ 0.29681352  0.63257413  0.17248881] 1
    True
    [ 0.22949674  0.72673313  0.14631401] 1
    True
    [ 0.4688456   0.11510779  0.43042847] 0
    True
    [ 0.23859275  0.3636267   0.37337131] 2
    True
    [ 0.43516508  0.07872227  0.57581066] 2
    True
    [ 0.42162891  0.12553052  0.44223457] 0
    False
    [ 0.19809478  0.77017482  0.14580304] 1
    True
    [ 0.47770693  0.10810706  0.47880266] 0
    False
    [ 0.46837617  0.23202502  0.28004671] 1
    False
    [ 0.3516865   0.64759058  0.11956896] 1
    True
    [ 0.40524935  0.24322665  0.36009768] 0
    True
    6000

    /anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:6: RuntimeWarning: overflow encountered in exp

```python
####### Test phase #######
Error_Test=[]
N_correct=0

from itertools import product
###### Chooses patch and defines label #####
#for PP in range(0,len(Sequence)):
for PP in range(20000, 21000):
    SS=Sequence[PP]
    if SS<N_F:
        Class_label=np.array([1,0,0])
        inputPatch=Patches_F[SS]
        Int_RGB=np.mean(np.mean(Patches_F_RGB[SS,:,:,:], axis=0), axis=0)/255

    elif(SS>=N_F) and (SS<(N_F+N_C)):
        Class_label=np.array([0,1,0])
        inputPatch=Patches_C[SS-N_F]
        Int_RGB=np.mean(np.mean(Patches_C_RGB[SS-N_F,:,:,:], axis=0), axis=0)/255

    elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
        Class_label=np.array([0,0,1])
        inputPatch=Patches_W[SS-N_F-N_C]
        Int_RGB=np.mean(np.mean(Patches_W_RGB[SS-N_F-N_C,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
    H1=[]
    H2=[]
    H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
    #H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
    x=np.zeros(ClassAmount)
    f=np.zeros(ClassAmount)
    for r in range (0, len(C1)):
        H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
        H2.append(Pool(H1[r], P12))
        for b in range(0,N_branches):
            H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
            H4[r][b]=Pool(H3[r][b],P34)
    y=np.append([H4.flatten()], [Int_RGB])
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
    for k in range(0,ClassAmount):
        W_t=np.append([W[k].flatten()], [W2[k]])
        x[k]=np.inner(y, W_t)
        f[k]=Sigmoid(x[k]-Output_bias[k])
    f=f/np.sum((f))
    Error_Test.append(np.sum((Class_label-f)**2))
    if np.argmax(f)==np.argmax(Class_label):
        #print True
        N_correct=N_correct+1

Perc_corr=float(N_correct)/1000
print Perc_corr

```

    0.649

    /anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:6: RuntimeWarning: overflow encountered in exp

```python
# TEST PHASE WITHOUT COLOUR

# TRAINING PHASE
#delta_H4=np.zeros((len(C1), N_branches, S_H4, S_H4))
#delta_H3=np.zeros((len(C1), N_branches, S_H4, S_H4))

#ERROR_cv2=np.zeros([1])

from itertools import product
for CROSSES in range(0,1):
    C2=C2_INIT
    W=W_INIT
    #W2=W2_INIT
    n_W=1
    n_C2=10*10**-2

    Sample_iterations=0
    N_1000=0

###### Chooses patch and defines label #####
#for PP in range(0,len(Sequence)):
    for PP in range(0,6000):
        SS=Sequence[PP]
        #SS=14000
        if SS<N_F:
            Class_label=np.array([1,0,0])
            inputPatch=Patches_F[SS]

        elif(SS>=N_F) and (SS<(N_F+N_C)):
            Class_label=np.array([0,1,0])
            inputPatch=Patches_C[SS-N_F]

        elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
            Class_label=np.array([0,0,1])
            inputPatch=Patches_W[SS-N_F-N_C]

    #else:
    #    Class_label=np.array([0,0,0,1])
    #    inputPatch=Patches_G[SS-N_F-N_C-N_W]
    #    Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
        H1=[]
        H2=[]
        H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
        H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
        x=np.zeros(ClassAmount)
        f=np.zeros(ClassAmount)

        II=1
        ITER=0
        while II==1:
            for r in range (0, len(C1)):
                H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
                H2.append(Pool(H1[r], P12))
        #From here on BP trakes place!
                for b in range(0,N_branches):
                    H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                    H4[r][b]=Pool(H3[r][b],P34)
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
            y=H4.flatten()
            for k in range(0,ClassAmount):
                W_t=W[k].flatten()
                x[k]=np.inner(y, W_t)
                f[k]=Sigmoid(x[k]-Output_bias[k])
            #f=f/np.sum((f))

            ###### Back-propagation #####
            # First learning the delta's
            delta_H4=np.zeros([ClassAmount,len(C1),N_branches,S_H4,S_H4])
            e_k=f-Class_label
            delta_k=e_k*Sigmoid_dx(x)
            #Output_bias=Output_bias[k]+n_bias*e_k
            for k in range(0, ClassAmount):
            #update weights output layer
                W[k]=W[k]-n_W*delta_k[k]*H4
                delta_H4[i]=delta_k[i]*W[i]
            delta_H4=np.sum(delta_H4, axis=0)
            delta_H3=(float(1)/10)*delta_H4
            C2_diff=np.zeros([len(C1),N_branches, Size_C2, Size_C2])
            for r in range(0, len(C1)):
                C2_t=np.array([[delta_H3[r][:]*H2[r][(0+u):(4+u),(0+v):(4+v)] for u in range(0,Size_C2)] for v in range (0,Size_C2)])
                C2_t=np.sum(np.sum(C2_t, axis=4),axis=3)
                C2_t=np.rollaxis(C2_t,2)
                C2_diff[r]=-n_C2*C2_t
            C2=C2+C2_diff
            #print f
            ERROR=np.sum((Class_label-f)**2)
            ITER=ITER+1

            if ERROR<0.55 or ITER>4:
                II=0
        print f, np.argmax(Class_label)
        if np.argmax(f)==np.argmax(Class_label):
            print True
        else:
            print False

#        Sample_iterations=Sample_iterations+1
#        if (Sample_iterations-(1000*N_1000))==1000:
#            print Sample_iterations
#            N_1000=N_1000+1
#            n_W=0.5*n_W
#            n_C2=0.5*n_C2

        Sample_iterations=Sample_iterations+1
        if Sample_iterations>1000:
            n_W=0.7
            n_C2=0.7*10*10**-2
        if Sample_iterations>2000:
            n_W=0.7*0.7
            n_C2=0.7*0.7*10*10**-2

        if Sample_iterations>3000:
            n_W=0.7*0.7*0.7
            n_C2=0.7*0.7*0.7*10*10**-2

        if Sample_iterations>5000:
            n_W=0.2
            n_C2=0.02

        #if Sample_iterations>7500:
        #    n_W=0.1
        #    n_C2=0.001

        #if Sample_iterations>10000:
        #    n_W=0.01
        #    n_C2=0.0005

    print "Training completed"
    ###### test phase!
    N_correct=0

    for PP in range(6000, 7000):
        SS=Sequence[PP]
        if SS<N_F:
            Class_label=np.array([1,0,0])
            inputPatch=Patches_F[SS]

        elif(SS>=N_F) and (SS<(N_F+N_C)):
            Class_label=np.array([0,1,0])
            inputPatch=Patches_C[SS-N_F]

        elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
            Class_label=np.array([0,0,1])
            inputPatch=Patches_W[SS-N_F-N_C]

        # else:
        #     Class_label=np.array([0,0,0,1])
        #     inputPatch=Patches_G[SS-N_F-N_C-N_W]
        #     Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
        H1=[]
        H2=[]
        H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
        H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
        x=np.zeros(ClassAmount)
        f=np.zeros(ClassAmount)
        for r in range (0, len(C1)):
            H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
            H2.append(Pool(H1[r], P12))
            for b in range(0,N_branches):
                H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                H4[r][b]=Pool(H3[r][b],P34)
        y=H4.flatten()
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
        for k in range(0,ClassAmount):
            W_t=W[k].flatten()
            x[k]=np.inner(y, W_t)
            f[k]=Sigmoid(x[k]-Output_bias[k])
        f=f/np.sum((f))
        if np.argmax(f)==np.argmax(Class_label):
            #print True
            N_correct=N_correct+1

    Perc_corr=float(N_correct)/1000
    print Perc_corr
    #ERROR_cv2[CROSSES]=Perc_corr
    #Sequence=np.roll(Sequence,2262)
```

    [ 0.27959365  0.41338191  0.3140045 ] 1
    True
    [ 0.4184772   0.36117519  0.23754256] 0
    True
    [ 0.37973085  0.32236441  0.33080257] 0
    True
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    False
    [ 0.30362824  0.36304391  0.33096758] 0
    False
    [ 0.26990855  0.40091926  0.33785747] 1
    True
    [ 0.33908909  0.32741609  0.34411619] 1
    False
    [ 0.32718648  0.32278794  0.35171778] 2
    True
    [ 0.30743389  0.33477025  0.35141576] 1
    False
    [ 0.29976374  0.37861356  0.31538363] 1
    True
    [ 0.3325665   0.35997387  0.30076937] 0
    False
    [ 0.3051557   0.31752553  0.3688261 ] 0
    False
    [ 0.26539077  0.35206167  0.38592151] 1
    False
    [ 0.35317696  0.30312161  0.33400665] 0
    True
    [ 0.33452006  0.29521822  0.36541613] 2
    True
    [ 0.2971745   0.33688391  0.35928177] 2
    True
    [ 0.30633799  0.33379448  0.36307294] 0
    False
    [ 0.33613202  0.36011772  0.30071488] 1
    True
    [ 0.35034172  0.308674    0.34067598] 0
    True
    [ 0.37285524  0.33434627  0.30646525] 0
    True
    [ 0.25983706  0.36756931  0.38080746] 1
    False
    [ 0.35295196  0.30443393  0.34585081] 0
    True
    [ 0.33590704  0.34413473  0.32215573] 2
    False
    [ 0.35749462  0.31407342  0.32113612] 2
    False
    [ 0.34827562  0.28708264  0.36984337] 2
    True
    [ 0.26318554  0.41058261  0.34180559] 1
    True
    [ 0.34321437  0.33069678  0.32444324] 0
    True
    [ 0.3252671   0.30825461  0.35133555] 1
    False
    [ 0.28919794  0.34009249  0.372962  ] 2
    True
    [ 0.28043142  0.37134222  0.37881217] 2
    True
    [ 0.32768486  0.30314652  0.35655127] 2
    True
    [ 0.21526908  0.42041081  0.39552825] 1
    True
    [ 0.2296878   0.39144018  0.39143932] 2
    False
    [ 0.28716252  0.28441551  0.41824304] 2
    True
    [ 0.28818434  0.37992481  0.34704082] 0
    False
    [ 0.30925988  0.30991164  0.37253702] 0
    False
    [ 0.30876938  0.31054666  0.37777974] 2
    True
    [ 0.27673984  0.31489971  0.40578675] 0
    False
    [ 0.3713698   0.24777915  0.38015092] 0
    False
    [ 0.38319784  0.2398583   0.38032575] 2
    False
    [ 0.38412714  0.23382368  0.38856846] 0
    False
    [ 0.2751789   0.34495381  0.38588252] 2
    True
    [ 0.3249301   0.30372084  0.37127067] 1
    False
    [ 0.38472636  0.2983096   0.3157236 ] 1
    False
    [ 0.38435746  0.24156203  0.38512269] 2
    True
    [ 0.3209911   0.26150131  0.42237835] 0
    False
    [ 0.39946803  0.29076539  0.31980053] 0
    True
    [ 0.31890304  0.31633186  0.36576175] 1
    False
    [ 0.32110778  0.31479822  0.36038677] 1
    False
    [ 0.34199903  0.31247603  0.34873832] 2
    True
    [ 0.33429887  0.29943981  0.3583903 ] 2
    True
    [ 0.33482575  0.28997912  0.38466856] 0
    False
    [ 0.29730913  0.34759444  0.36488311] 1
    False
    [ 0.36099046  0.28030826  0.37807701] 0
    False
    [ 0.30001428  0.30145014  0.3878051 ] 2
    True
    [ 0.34742623  0.26581427  0.39491576] 2
    True
    [ 0.3103295   0.27356306  0.41560675] 1
    False
    [ 0.30228201  0.33606635  0.37220095] 2
    True
    [ 0.3161026   0.31820666  0.36451625] 1
    False
    [ 0.29441221  0.30482438  0.40067915] 2
    True
    [ 0.29415377  0.311431    0.39843896] 2
    True
    [ 0.33539725  0.31136346  0.35794583] 2
    True
    [ 0.32700699  0.27573031  0.39792681] 2
    True
    [ 0.34885939  0.22906761  0.44071459] 0
    False
    [ 0.28038501  0.30377148  0.40665824] 0
    False
    [ 0.30619653  0.31971353  0.3846524 ] 1
    False
    [ 0.41115164  0.26584918  0.31668323] 1
    False
    [ 0.26952186  0.37517072  0.35209824] 1
    True
    [ 0.30607342  0.37588423  0.33419526] 1
    True
    [ 0.31896787  0.36428521  0.30591983] 1
    True
    [ 0.31001723  0.39582467  0.29242087] 1
    True
    [ 0.29513142  0.35086661  0.36328608] 2
    True
    [ 0.33000326  0.31205068  0.35861762] 0
    False
    [ 0.32958854  0.35627391  0.32205967] 1
    True
    [ 0.28558272  0.35677713  0.35440143] 0
    False
    [ 0.29391995  0.38707187  0.31139868] 2
    False
    [ 0.34356085  0.38172993  0.28268895] 0
    False
    [ 0.30539025  0.3392588   0.35269361] 0
    False
    [ 0.32867526  0.31936898  0.35164839] 0
    False
    [ 0.32249974  0.29488556  0.38811853] 2
    True
    [ 0.38157209  0.29215854  0.31819513] 0
    True
    [ 0.38565291  0.2631507   0.35998068] 2
    False
    [ 0.30984095  0.33558575  0.3529806 ] 1
    False
    [ 0.37026414  0.27020238  0.33870836] 2
    False
    [ 0.2936039   0.3813817   0.32589577] 1
    True
    [ 0.28391177  0.36820697  0.35281366] 2
    False
    [ 0.2580392   0.39119636  0.3548221 ] 1
    True
    [ 0.32235972  0.31946802  0.35027814] 1
    False
    [ 0.29885049  0.33754132  0.37564293] 2
    True
    [ 0.33086088  0.33207162  0.34672434] 0
    False
    [ 0.27958046  0.32030763  0.39985224] 1
    False
    [ 0.26482458  0.39455752  0.34072483] 1
    True
    [ 0.33060008  0.33170751  0.31743717] 0
    False
    [ 0.27317121  0.41381642  0.31061125] 0
    False
    [ 0.39953926  0.27568696  0.32896367] 2
    False
    [ 0.33370274  0.35339457  0.30947811] 1
    True
    [ 0.27229454  0.39959708  0.33291026] 1
    True
    [ 0.38585108  0.31948616  0.29083819] 1
    False
    [ 0.28112169  0.40552197  0.31102368] 1
    True
    [ 0.2906221   0.38551297  0.30725537] 1
    True
    [ 0.30410559  0.37874231  0.32176843] 0
    False
    [ 0.31792543  0.36133117  0.31260829] 0
    False
    [ 0.2982465   0.39364748  0.3034194 ] 1
    True
    [ 0.32236695  0.35788623  0.33583335] 1
    True
    [ 0.3520023   0.3769543   0.27683093] 1
    True
    [ 0.28542769  0.38534438  0.32124205] 0
    False
    [ 0.28433351  0.40229034  0.32103183] 1
    True
    [ 0.34559388  0.35367345  0.29357349] 0
    False
    [ 0.27508439  0.37155657  0.35882124] 2
    False
    [ 0.34335412  0.3503787   0.29930798] 2
    False
    [ 0.32193214  0.36488644  0.31758266] 0
    False
    [ 0.31990599  0.37657318  0.3151169 ] 0
    False
    [ 0.33794687  0.36865802  0.29190108] 1
    True
    [ 0.35199452  0.37112125  0.2759091 ] 1
    True
    [ 0.33772981  0.36404641  0.304642  ] 2
    False
    [ 0.31783185  0.4054929   0.27456569] 1
    True
    [ 0.30793618  0.37688341  0.31806764] 0
    False
    [ 0.35756038  0.35069702  0.28245833] 0
    True
    [ 0.32510498  0.38744046  0.28671019] 1
    True
    [ 0.33912758  0.40974682  0.2577427 ] 0
    False
    [ 0.38350572  0.32542982  0.30076777] 2
    False
    [ 0.36576556  0.32972528  0.30914579] 1
    False
    [ 0.40954081  0.29931775  0.27605715] 1
    False
    [ 0.28922876  0.39738404  0.3119184 ] 1
    True
    [ 0.27780277  0.40290797  0.32820735] 1
    True
    [ 0.38578667  0.3375646   0.29340343] 0
    True
    [ 0.33124821  0.42415227  0.25458449] 1
    True
    [ 0.32506469  0.36950567  0.29477795] 1
    True
    [ 0.37611613  0.40630542  0.22498377] 1
    True
    [ 0.30945461  0.41141794  0.28461319] 1
    True
    [ 0.33324832  0.39340598  0.27819801] 1
    True
    [ 0.35900823  0.39263372  0.25416875] 1
    True
    [ 0.33397327  0.40927645  0.26023579] 1
    True
    [ 0.2997108   0.39704348  0.30618045] 1
    True
    [ 0.31237881  0.42753495  0.27695011] 2
    False
    [ 0.34289315  0.3914569   0.27967987] 0
    False
    [ 0.34224856  0.42889231  0.2480051 ] 0
    False
    [ 0.27316625  0.47617774  0.27089953] 0
    False
    [ 0.36706585  0.3832062   0.2484661 ] 2
    False
    [ 0.37091255  0.3675861   0.26256737] 2
    False
    [ 0.38822501  0.34361621  0.26813017] 0
    True
    [ 0.35318059  0.38738508  0.2724795 ] 0
    False
    [ 0.29593016  0.36671602  0.34471166] 1
    True
    [ 0.32840077  0.39504692  0.28282586] 0
    False
    [ 0.31290445  0.38539346  0.30791962] 2
    False
    [ 0.32669768  0.40800439  0.27571257] 2
    False
    [ 0.29021162  0.41624255  0.28800627] 2
    False
    [ 0.38478396  0.30032032  0.30079198] 0
    True
    [ 0.28938237  0.399273    0.31402762] 2
    False
    [ 0.37705461  0.33419492  0.28954271] 0
    True
    [ 0.30600863  0.41063873  0.28685343] 1
    True
    [ 0.34781298  0.34925514  0.31425171] 1
    True
    [ 0.34663171  0.31010496  0.34442885] 2
    False
    [ 0.34704712  0.37563231  0.28224352] 0
    False
    [ 0.31293946  0.35608559  0.31931501] 1
    True
    [ 0.31305211  0.37666349  0.30922239] 0
    False
    [ 0.35518731  0.32935667  0.31577024] 2
    False
    [ 0.30303112  0.33962463  0.36640766] 2
    True
    [ 0.2885138   0.39292703  0.31061035] 1
    True
    [ 0.34985488  0.36455572  0.2873911 ] 1
    True
    [ 0.27489803  0.40943475  0.30670888] 2
    False
    [ 0.28665972  0.32814881  0.38100478] 2
    True
    [ 0.37483149  0.31095155  0.3110215 ] 1
    False
    [ 0.24798002  0.38478223  0.36790062] 2
    False
    [ 0.35830332  0.33006253  0.30890799] 0
    True
    [ 0.34128069  0.3287083   0.32080387] 1
    False
    [ 0.24904022  0.40584255  0.35196783] 1
    True
    [ 0.29964095  0.36373177  0.33194712] 0
    False
    [ 0.32489204  0.35071275  0.31472552] 1
    True
    [ 0.30797906  0.35862106  0.33158734] 0
    False
    [ 0.28784585  0.4144915   0.29618721] 1
    True
    [ 0.28814009  0.39010514  0.32637427] 1
    True
    [ 0.25719379  0.42814041  0.31751696] 2
    False
    [ 0.35393452  0.33287007  0.31283507] 0
    True
    [ 0.25773428  0.39869595  0.35848482] 2
    False
    [ 0.23954351  0.42820201  0.35569339] 2
    False
    [ 0.37764265  0.33482261  0.29436068] 1
    False
    [ 0.33628895  0.33109705  0.34201829] 1
    False
    [ 0.30543893  0.40148213  0.29295218] 0
    False
    [ 0.35216422  0.3506035   0.2956587 ] 1
    False
    [ 0.2746503   0.39591019  0.33624633] 1
    True
    [ 0.29215524  0.40913827  0.30891164] 1
    True
    [ 0.28907803  0.39953525  0.319717  ] 1
    True
    [ 0.28863946  0.42284067  0.29160179] 0
    False
    [ 0.2910485   0.40172014  0.30922941] 2
    False
    [ 0.32447494  0.36141474  0.3009122 ] 2
    False
    [ 0.36043392  0.37021852  0.26878433] 0
    False
    [ 0.27017665  0.43457784  0.30059839] 1
    True
    [ 0.27963845  0.39865386  0.32698404] 0
    False
    [ 0.32038709  0.37877692  0.29684875] 1
    True
    [ 0.34675976  0.35278866  0.28812664] 1
    True
    [ 0.28818776  0.42046356  0.29777269] 0
    False
    Training completed
    0.321

    /anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:6: RuntimeWarning: overflow encountered in exp

```python
C2_INIT
```

    array([[[[ 0.77361321,  0.93182158,  0.70505992,  0.80422591,  0.1412422 ],
             [ 0.82290848,  0.63917435,  0.90347831,  0.87803979,  0.37720469],
             [ 0.18694892,  0.6482777 ,  0.80398425,  0.61711257,  0.25713158],
             [ 0.52051612,  0.12288631,  0.67118044,  0.610306  ,  0.62816537],
             [ 0.98065426,  0.11886239,  0.50349261,  0.26714588,  0.02563766]],

            [[ 0.38593663,  0.77153711,  0.94189197,  0.91435221,  0.52416684],
             [ 0.33415833,  0.96887456,  0.08432545,  0.43806018,  0.42834064],
             [ 0.07147305,  0.92917761,  0.0655414 ,  0.69054255,  0.14514601],
             [ 0.8880909 ,  0.48253988,  0.4782931 ,  0.29879164,  0.09653021],
             [ 0.14523237,  0.2919442 ,  0.14693784,  0.93384983,  0.68102344]],

            [[ 0.11339229,  0.04551164,  0.8511457 ,  0.97720889,  0.07282508],
             [ 0.20138635,  0.51323846,  0.89074582,  0.6704784 ,  0.34351992],
             [ 0.94680068,  0.00263861,  0.52705039,  0.04150781,  0.1981522 ],
             [ 0.79174949,  0.83560386,  0.12828078,  0.57988589,  0.60540605],
             [ 0.65793304,  0.19686322,  0.15036307,  0.0290918 ,  0.10889995]]],

           [[[ 0.12329405,  0.93355471,  0.01845181,  0.78831938,  0.88435157],
             [ 0.22768363,  0.40246916,  0.49533672,  0.96084282,  0.68761556],
             [ 0.12913785,  0.82011523,  0.35992067,  0.38547226,  0.95589189],
             [ 0.36555491,  0.75952693,  0.76139844,  0.5493738 ,  0.30694879],
             [ 0.17487926,  0.89262204,  0.02650875,  0.32710238,  0.27231143]],

            [[ 0.27191789,  0.46864823,  0.02734897,  0.22309678,  0.04973466],
             [ 0.27447282,  0.52358444,  0.55812888,  0.04229138,  0.65413115],
             [ 0.51466104,  0.42695084,  0.78178729,  0.45365784,  0.50726015],
             [ 0.34256099,  0.50863581,  0.64283721,  0.51648786,  0.37265825],
             [ 0.35811952,  0.06018938,  0.50453329,  0.98636914,  0.78066703]],

            [[ 0.41548634,  0.06490975,  0.92927259,  0.01768628,  0.4105494 ],
             [ 0.46362558,  0.80421379,  0.83765648,  0.78090424,  0.13987367],
             [ 0.11434628,  0.88826959,  0.81857983,  0.09339392,  0.62799575],
             [ 0.01830243,  0.30885712,  0.75598387,  0.46806943,  0.45343679],
             [ 0.42458233,  0.43017579,  0.43951806,  0.95409968,  0.8923725 ]]]])

```python
####### Test phase #######
Error_Test=[]
N_correct=0

from itertools import product
###### Chooses patch and defines label #####
#for PP in range(0,len(Sequence)):
for PP in range(20000, 21000):
    SS=Sequence[PP]
    if SS<N_F:
        Class_label=np.array([1,0,0])
        inputPatch=Patches_F[SS]
        Int_RGB=np.mean(np.mean(Patches_F_RGB[SS,:,:,:], axis=0), axis=0)/255

    elif(SS>=N_F) and (SS<(N_F+N_C)):
        Class_label=np.array([0,1,0])
        inputPatch=Patches_C[SS-N_F]
        Int_RGB=np.mean(np.mean(Patches_C_RGB[SS-N_F,:,:,:], axis=0), axis=0)/255

    elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
        Class_label=np.array([0,0,1])
        inputPatch=Patches_W[SS-N_F-N_C]
        Int_RGB=np.mean(np.mean(Patches_W_RGB[SS-N_F-N_C,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
    H1=[]
    H2=[]
    H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
    #H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
    x=np.zeros(ClassAmount)
    f=np.zeros(ClassAmount)
    for r in range (0, len(C1)):
        H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
        H2.append(Pool(H1[r], P12))
        for b in range(0,N_branches):
            H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
            H4[r][b]=Pool(H3[r][b],P34)
    y=np.append([H4.flatten()], [Int_RGB])
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
    for k in range(0,ClassAmount):
        W_t=np.append([W[k].flatten()], [W2[k]])
        x[k]=np.inner(y, W_t)
        f[k]=Sigmoid(x[k]-Output_bias[k])
    f=f/np.sum((f))
    Error_Test.append(np.sum((Class_label-f)**2))
    if np.argmax(f)==np.argmax(Class_label):
        #print True
        N_correct=N_correct+1

Perc_corr=float(N_correct)/1000
print Perc_corr

```

    0.655

    /anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:6: RuntimeWarning: overflow encountered in exp

```python
# CROSS VALIDATION!!

# TRAINING PHASE
#delta_H4=np.zeros((len(C1), N_branches, S_H4, S_H4))
#delta_H3=np.zeros((len(C1), N_branches, S_H4, S_H4))

ERROR_cv=np.zeros([10])

from itertools import product
for CROSSES in range(0,10):
    C2=C2_INIT
    W=W_INIT
    W2=W2_INIT
    n_W=1
    n_C2=1.5*10**-2

    Sample_iterations=0
    N_1000=0

###### Chooses patch and defines label #####
#for PP in range(0,len(Sequence)):
    for PP in range(0,20353):
        SS=Sequence[PP]
        #SS=14000
        if SS<N_F:
            Class_label=np.array([1,0,0])
            inputPatch=Patches_F[SS]
            Int_RGB=np.mean(np.mean(Patches_F_RGB[SS,:,:,:], axis=0), axis=0)/255

        elif(SS>=N_F) and (SS<(N_F+N_C)):
            Class_label=np.array([0,1,0])
            inputPatch=Patches_C[SS-N_F]
            Int_RGB=np.mean(np.mean(Patches_C_RGB[SS-N_F,:,:,:], axis=0), axis=0)/255

        elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
            Class_label=np.array([0,0,1])
            inputPatch=Patches_W[SS-N_F-N_C]
            Int_RGB=np.mean(np.mean(Patches_W_RGB[SS-N_F-N_C,:,:,:], axis=0), axis=0)/255

    #else:
    #    Class_label=np.array([0,0,0,1])
    #    inputPatch=Patches_G[SS-N_F-N_C-N_W]
    #    Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
        H1=[]
        H2=[]
        H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
        H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
        x=np.zeros(ClassAmount)
        f=np.zeros(ClassAmount)

        II=1
        ITER=0
        while II==1:

            for r in range (0, len(C1)):
                H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
                H2.append(Pool(H1[r], P12))
        #From here on BP trakes place!
                for b in range(0,N_branches):
                    H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                    H4[r][b]=Pool(H3[r][b],P34)
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
            y=np.append([H4.flatten()], [Int_RGB])
            for k in range(0,ClassAmount):
                W_t=np.append([W[k].flatten()], [W2[k]])
                x[k]=np.inner(y, W_t)
                f[k]=Sigmoid(x[k]-Output_bias[k])

            ###### Back-propagation #####
            # First learning the delta's
            delta_H4=np.zeros([ClassAmount,len(C1),N_branches,S_H4,S_H4])
            e_k=f-Class_label
            delta_k=e_k*Sigmoid_dx(x)
            #Output_bias=Output_bias[k]+n_bias*e_k
            for k in range(0, ClassAmount):
            #update weights output layer
                W[k]=W[k]-n_W*delta_k[k]*H4
                W2[k]=W2[k]-n_W*delta_k[k]*Int_RGB
                delta_H4[i]=delta_k[i]*W[i]
            delta_H4=np.sum(delta_H4, axis=0)
            delta_H3=(float(1)/10)*delta_H4
            C2_diff=np.zeros([len(C1),N_branches, Size_C2, Size_C2])
            for r in range(0, len(C1)):
                C2_t=np.array([[delta_H3[r][:]*H2[r][(0+u):(4+u),(0+v):(4+v)] for u in range(0,Size_C2)] for v in range (0,Size_C2)])
                C2_t=np.sum(np.sum(C2_t, axis=4),axis=3)
                C2_t=np.rollaxis(C2_t,2)
                C2_diff[r]=-n_C2*C2_t
            C2=C2+C2_diff
            #print f
            ERROR=np.sum((Class_label-f)**2)
            ITER=ITER+1

            if ERROR<0.55 or ITER>4:
                II=0
        #print f, np.argmax(Class_label)
        #if np.argmax(f)==np.argmax(Class_label):
            #print True
        #else:
            #print False

        Sample_iterations=Sample_iterations+1
        if Sample_iterations>1000:
            n_W=0.7
            n_C2=0.7*1.5*10**-2
        if Sample_iterations>2000:
            n_W=0.7*0.7
            n_C2=0.7*0.7*1.5*10**-2

        if Sample_iterations>3000:
            n_W=0.7*0.7*0.7
            n_C2=0.7*0.7*0.7*1.5*10**-2

        if Sample_iterations>5000:
            n_W=0.2
            n_C2=0.0025

        if Sample_iterations>7500:
            n_W=0.1
            n_C2=0.001

        if Sample_iterations>10000:
            n_W=0.01
            n_C2=0.0005

    print "Training completed"
    ###### test phase!
    N_correct=0
    for PP in range(20353, N_total):
        SS=Sequence[PP]
        if SS<N_F:
            Class_label=np.array([1,0,0])
            inputPatch=Patches_F[SS]
            Int_RGB=np.mean(np.mean(Patches_F_RGB[SS,:,:,:], axis=0), axis=0)/255

        elif(SS>=N_F) and (SS<(N_F+N_C)):
            Class_label=np.array([0,1,0])
            inputPatch=Patches_C[SS-N_F]
            Int_RGB=np.mean(np.mean(Patches_C_RGB[SS-N_F,:,:,:], axis=0), axis=0)/255

        elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
            Class_label=np.array([0,0,1])
            inputPatch=Patches_W[SS-N_F-N_C]
            Int_RGB=np.mean(np.mean(Patches_W_RGB[SS-N_F-N_C,:,:,:], axis=0), axis=0)/255

        # else:
        #     Class_label=np.array([0,0,0,1])
        #     inputPatch=Patches_G[SS-N_F-N_C-N_W]
        #     Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
        H1=[]
        H2=[]
        H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
        H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
        x=np.zeros(ClassAmount)
        f=np.zeros(ClassAmount)
        for r in range (0, len(C1)):
            H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
            H2.append(Pool(H1[r], P12))
            for b in range(0,N_branches):
                H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                H4[r][b]=Pool(H3[r][b],P34)
        y=np.append([H4.flatten()], [Int_RGB])
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
        for k in range(0,ClassAmount):
            W_t=np.append([W[k].flatten()], [W2[k]])
            x[k]=np.inner(y, W_t)
            f[k]=Sigmoid(x[k]-Output_bias[k])
        f=f/np.sum((f))
        if np.argmax(f)==np.argmax(Class_label):
            #print True
            N_correct=N_correct+1

    Perc_corr=float(N_correct)/(2262)
    print Perc_corr
    ERROR_cv[CROSSES]=Perc_corr
    N_correct=0
    Sequence=np.roll(Sequence,2262)

```

```python
# CROSS VALIDATION!! WITHOUT COLOUR

# TRAINING PHASE
#delta_H4=np.zeros((len(C1), N_branches, S_H4, S_H4))
#delta_H3=np.zeros((len(C1), N_branches, S_H4, S_H4))

ERROR_cv2=np.zeros([10])

from itertools import product
for CROSSES in range(0,10):
    C2=C2_INIT
    W=W_INIT
    W2=W2_INIT
    n_W=1
    n_C2=1.5*10**-2

    Sample_iterations=0
    N_1000=0

###### Chooses patch and defines label #####
#for PP in range(0,len(Sequence)):
    for PP in range(0,20353):
        SS=Sequence[PP]
        #SS=14000
        if SS<N_F:
            Class_label=np.array([1,0,0])
            inputPatch=Patches_F[SS]

        elif(SS>=N_F) and (SS<(N_F+N_C)):
            Class_label=np.array([0,1,0])
            inputPatch=Patches_C[SS-N_F]

        elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
            Class_label=np.array([0,0,1])
            inputPatch=Patches_W[SS-N_F-N_C]

    #else:
    #    Class_label=np.array([0,0,0,1])
    #    inputPatch=Patches_G[SS-N_F-N_C-N_W]
    #    Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
        H1=[]
        H2=[]
        H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
        H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
        x=np.zeros(ClassAmount)
        f=np.zeros(ClassAmount)

        II=1
        ITER=0
        while II==1:

            for r in range (0, len(C1)):
                H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
                H2.append(Pool(H1[r], P12))
        #From here on BP trakes place!
                for b in range(0,N_branches):
                    H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                    H4[r][b]=Pool(H3[r][b],P34)
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
            y=H4.flatten()
            for k in range(0,ClassAmount):
                W_t=W[k].flatten()
                x[k]=np.inner(y, W_t)
                f[k]=Sigmoid(x[k]-Output_bias[k])
            f=f/np.sum((f))

            ###### Back-propagation #####
            # First learning the delta's
            delta_H4=np.zeros([ClassAmount,len(C1),N_branches,S_H4,S_H4])
            e_k=f-Class_label
            delta_k=e_k*Sigmoid_dx(x)
            #Output_bias=Output_bias[k]+n_bias*e_k
            for k in range(0, ClassAmount):
            #update weights output layer
                W[k]=W[k]-n_W*delta_k[k]*H4
                delta_H4[i]=delta_k[i]*W[i]
            delta_H4=np.sum(delta_H4, axis=0)
            delta_H3=(float(1)/10)*delta_H4
            C2_diff=np.zeros([len(C1),N_branches, Size_C2, Size_C2])
            for r in range(0, len(C1)):
                C2_t=np.array([[delta_H3[r][:]*H2[r][(0+u):(4+u),(0+v):(4+v)] for u in range(0,Size_C2)] for v in range (0,Size_C2)])
                C2_t=np.sum(np.sum(C2_t, axis=4),axis=3)
                C2_t=np.rollaxis(C2_t,2)
                C2_diff[r]=-n_C2*C2_t
            C2=C2+C2_diff
            #print f
            ERROR=np.sum((Class_label-f)**2)
            ITER=ITER+1

            if ERROR<0.55 or ITER>4:
                II=0
        #print f, np.argmax(Class_label)
        #if np.argmax(f)==np.argmax(Class_label):
            #print True
        #else:
            #print False

#        Sample_iterations=Sample_iterations+1
#        if (Sample_iterations-(1000*N_1000))==1000:
#            print Sample_iterations
#            N_1000=N_1000+1
#            n_W=0.5*n_W
#            n_C2=0.5*n_C2

        Sample_iterations=Sample_iterations+1
        if Sample_iterations>1000:
            n_W=0.7
            n_C2=0.7*1.5*10**-2
        if Sample_iterations>2000:
            n_W=0.7*0.7
            n_C2=0.7*0.7*1.5*10**-2

        if Sample_iterations>3000:
            n_W=0.7*0.7*0.7
            n_C2=0.7*0.7*0.7*1.5*10**-2

        if Sample_iterations>5000:
            n_W=0.2
            n_C2=0.0025

        if Sample_iterations>7500:
            n_W=0.1
            n_C2=0.001

        if Sample_iterations>10000:
            n_W=0.01
            n_C2=0.0005

    print "Training completed"
    ###### test phase!
    N_correct=0

    for PP in range(20353, N_total):
        SS=Sequence[PP]
        if SS<N_F:
            Class_label=np.array([1,0,0])
            inputPatch=Patches_F[SS]

        elif(SS>=N_F) and (SS<(N_F+N_C)):
            Class_label=np.array([0,1,0])
            inputPatch=Patches_C[SS-N_F]

        elif(SS>=(N_F+N_C)) and (SS<N_F+N_C+N_W):
            Class_label=np.array([0,0,1])
            inputPatch=Patches_W[SS-N_F-N_C]

        # else:
        #     Class_label=np.array([0,0,0,1])
        #     inputPatch=Patches_G[SS-N_F-N_C-N_W]
        #     Int_RGB=np.mean(np.mean(Patches_G_RGB[SS-N_F-N_C-N_W,:,:,:], axis=0), axis=0)/255

    ### Layer 1 ###
        H1=[]
        H2=[]
        H3=np.zeros((len(C1), N_branches, S_H3,S_H3))
        H4=np.zeros((len(C1), N_branches, S_H4,S_H4))
        x=np.zeros(ClassAmount)
        f=np.zeros(ClassAmount)
        for r in range (0, len(C1)):
            H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
            H2.append(Pool(H1[r], P12))
            for b in range(0,N_branches):
                H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
                H4[r][b]=Pool(H3[r][b],P34)
        y=H4.flatten()
    #Now we have 3x3x4x4 inputs, connected to the 3 output nodes
        for k in range(0,ClassAmount):
            W_t=W[k].flatten()
            x[k]=np.inner(y, W_t)
            f[k]=Sigmoid(x[k]-Output_bias[k])
        f=f/np.sum((f))
        if np.argmax(f)==np.argmax(Class_label):
            #print True
            N_correct=N_correct+1

    Perc_corr=float(N_correct)/2262
    print Perc_corr
    ERROR_cv2[CROSSES]=Perc_corr
    Sequence=np.roll(Sequence,2262)
```

    Training completed
    0.345269672856
    Training completed
    0.349690539346
    Training completed
    0.349248452697
    Training completed
    0.357648099027
    Training completed
    0.343059239611
    Training completed
    0.371794871795
    Training completed
    0.360300618921
    Training completed
    0.366931918656
    Training completed
    0.353227232538
    Training completed
    0.359858532272

    /anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:6: RuntimeWarning: overflow encountered in exp

```python
print ERROR_cv
#print ERROR_cv2

Ave_CV_withRGB=np.mean(ERROR_cv)
Std_CV_withRGB=np.std(ERROR_cv)
Ave_CV_withoutRGB=np.mean(ERROR_cv2)
Std_CV_withoutRGB=np.std(ERROR_cv2)

with open("Ave_CV_withRGB.txt", 'w') as f:
    f.write(str(Ave_CV_withRGB))
with open("Std_CV_withRGB.txt", 'w') as f:
    f.write(str(Std_CV_withRGB))
with open("Ave_CV_withoutRGB.txt", 'w') as f:
    f.write(str(Ave_CV_withoutRGB))
with open("Std_CV_withoutRGB.txt", 'w') as f:
    f.write(str(Std_CV_withoutRGB))
```

    [ 0.66843501  0.67064545  0.68523431  0.64544651  0.66622458  0.65340407
      0.68567639  0.67639257  0.67992927  0.66312997]

```python
print Ave_CV_withRGB, Std_CV_withRGB, Ave_CV_withoutRGB, Std_CV_withoutRGB
```

    0.669451812555 0.0124775775099 0.355702917772 0.00883111652639

# save training parameters

```python
import pickle

# obj0, obj1, obj2 are created here...

# Saving the objects:
#with open('objs.pickle', 'w') as f:
#    pickle.dump([obj0, obj1, obj2], f)

# Getting back the objects:
#with open('objs.pickle') as f:
#    obj0, obj1, obj2 = pickle.load(f)

import pickle
file=open('W.txt','w')
pickle.dump(W,file)
file.close()

file=open('W2.txt','w')
pickle.dump(W2,file)
file.close()

file=open('Output_bias.txt','w')
pickle.dump(Output_bias,file)
file.close()

file=open('H3_bias.txt','w')
pickle.dump(H3_bias,file)
file.close()

file=open('C2.txt','w')
pickle.dump(C2,file)
file.close()
```

```python
W2
```

    array([[  5.29303417,  -2.70781514, -18.24611794],
           [ 40.34618459, -34.05035278,  10.92748976],
           [-27.57509419,  21.62962176,  -7.85044736],
           [  0.22459264,   0.27081064,   0.66933584]])

```python

```

```python

```