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ConvNetAbel.py
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# Copyright Abel Garcia. All Rights Reserved.
# https://github.com/abel-gr/AbelNN
import numpy as np
import copy as copy
import random
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import math
class ConvNetAbel:
__version__ = '1.2.1'
def __init__(self, hidden = [1], nEpochs = 1, learningRate=0.1, manualWeights=[],
debugLevel=1, rangeRandomWeight=None, showLogs=False, softmax=False,
activationFunction='leakyrelu', verbose = False, use='classification',
batch_size=1, batch_gradient='average', batch_mult=1, dropout=0, pre_norm=False,
shuffle=True, iterationDrop=0, convFilters = [32, 64, 128], convStride=2,
convFilterSizes=3, learningRateConv=0.001, convEpochs=10, kernel_initializer='he_normal'):
self.hiddenL = copy.deepcopy(hidden)
self.hiddenL2 = copy.deepcopy(hidden)
self.learningRate = learningRate
self.numEpochs = nEpochs
self.costs = [] # Costs list to check performance
self.debugWeights = []
self.meanCostByEpoch = []
self.hiddenWeights = []
self.manualWeights = manualWeights
self.debugMode = debugLevel
self.rangeRandomWeight = rangeRandomWeight
self.showLogs = showLogs
self.softmax = softmax
self.n_layer0 = -1
self.activationFunction = activationFunction
self.verbose = verbose
self.use = use
self.batch_size = batch_size
self.batch_gradient = batch_gradient
self.batch_mult = batch_mult
self.dropout = dropout
self.pre_norm = pre_norm
self.shuffle = shuffle
self.iterationDrop = iterationDrop
self.XavierInitialization = '1'
self.lastLayerNeurons = -1
# ConvNet:
self.convFilters = convFilters
self.filtersValues = [None] * len(convFilters)
self.convStride = convStride
self.convFilterSizes = convFilterSizes
self.learningRateConv = learningRateConv
self.convEpochs = convEpochs
self.kernel_initializer = kernel_initializer
# Conv2 with only one kernel
def conv2(self, x, kernel, stride=1):
output = [] #np.zeros((kernel.shape), dtype=np.float32)
kernel_l = kernel.shape[0]
kernel_size = kernel.shape[0] * kernel.shape[1]
c = int(kernel_l / 2)
for i in range(c, x.shape[0] - c, stride):
o = []
for j in range(c, x.shape[1] - c, stride):
i0 = i - c
j0 = j - c
i1 = i + c + 1
j1 = j + c + 1
o.append(np.sum(x[i0:i1, j0:j1] * kernel))
output.append(o)
output = np.asarray(output)
return output
# Convolution with multi-filters
def conv_filters(self, x, filters, stride=1, relu=False, mode='same'):
lex = len(x.shape)
lef = len(filters.shape)
if lex > lef:
print('conv_filters: The input array cannot have more dimensions than the filter array.')
return 0
output = []
kernel_l = filters.shape[0]
kernel_size = filters.shape[0] * filters.shape[1]
if lef == 2:
num_filters = 1
else:
num_filters = filters.shape[-1]
c = int(kernel_l / 2)
dim3 = False
evenShapeKernel = (kernel_l % 2 == 0)
if lex == 2:
dim2 = True
p0 = x.shape[0]
p1 = x.shape[1]
else:
# x parameter was the output of this method previously called
if lex == lef:
num_new_filters = int(num_filters / x.shape[-1])
if (num_new_filters % 2 != 0) and (num_filters % 2 == 0):
num_new_filters = num_new_filters - 1
if (num_new_filters == 0):
num_new_filters = 1
else: # It is the first convolutional layer of a color image
num_new_filters = num_filters
dim3 = True
dim2 = False
p0 = x.shape[0]
p1 = x.shape[1]
if mode == 'full':
fs0 = int(filters.shape[0] / 2)
fs1 = int(filters.shape[1] / 2)
max0 = p0 + fs0
max1 = p1 + fs1
ini0 = -1 * fs0
ini1 = -1 * fs1
elif mode == 'same':
max0 = p0
max1 = p1
ini0 = 0
ini1 = 0
elif mode == 'valid':
fs0 = int(filters.shape[0] / 2)
fs1 = int(filters.shape[1] / 2)
max0 = p0 - fs0
max1 = p1 - fs1
ini0 = fs0
ini1 = fs1
else:
print('Mode must be same, valid or full')
return 0
if evenShapeKernel and mode == 'valid':
max0 = max0 + 1
max1 = max1 + 1
for i in range(ini0, max0, stride):
o = []
for j in range(ini1, max1, stride):
i0 = i - c
j0 = j - c
i1 = i + c + 1
j1 = j + c + 1
if evenShapeKernel:
i0 = i0 + 1
j0 = j0 + 1
zero_padding_top = 0
zero_padding_bottom = 0
zero_padding_left = 0
zero_padding_right = 0
if i0 < 0:
zero_padding_top = abs(i0)
i0 = 0
if j0 < 0:
zero_padding_left = abs(j0)
j0 = 0
if i1 > p0:
zero_padding_bottom = i1 - p0
i1 = p0
if j1 > p1:
zero_padding_right = j1 - p1
j1 = p1
if dim2:
m = x[i0:i1, j0:j1]
#print('mshape:', m.shape, kernel_size, zero_padding_top, zero_padding_left)
# Zero padding:
m = np.pad(m, ((zero_padding_top,zero_padding_bottom),(zero_padding_left,zero_padding_right)), 'constant')
if lef != 2:
m = np.expand_dims(m, axis=-1)
m = np.repeat(m, num_filters, axis=-1)
else:
xi = x[i0:i1, j0:j1, :]
# Zero padding:
xi = np.pad(xi, ((zero_padding_top,zero_padding_bottom),(zero_padding_left,zero_padding_right),(0,0)), 'constant')
if dim3:
xi = np.expand_dims(xi, axis=-1)
m = np.repeat(xi, num_new_filters, axis=-1)
#print('M,F\n', m[:,:,0], filters[:,:,0])
#print(m.shape, filters.shape)
m = m * filters
#print('m*f\n', m[:,:,0])
m = np.sum(m, axis=0)
m = np.sum(m, axis=0)
if dim3:
m = np.sum(m, axis=0)
o.append(m)
output.append(o)
output = np.asarray(output)
if relu:
output[output < 0] = 0
return output
def kernelInitializer(self, i, ksize, inSize, outSize):
if 'xavier' in self.kernel_initializer:
if self.kernel_initializer == 'xavier_normal':
if len(ksize) == 4:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2],ksize[3]) * math.sqrt(2.0 / (inSize + outSize))
else:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2]) * math.sqrt(2.0 / (inSize + outSize))
elif self.kernel_initializer == 'xavier_uniform':
highVal = math.sqrt(6.0 / (inSize + outSize))
lowVal = -1 * highVal
self.filtersValues[i] = np.random.uniform(low=lowVal, high=highVal, size=ksize)
else:
if self.kernel_initializer == 'he_normal':
if len(ksize) == 4:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2],ksize[3]) * math.sqrt(2.0 / inSize)
else:
self.filtersValues[i] = np.random.randn(ksize[0],ksize[1],ksize[2]) * math.sqrt(2.0 / inSize)
elif self.kernel_initializer == 'he_uniform':
highVal = math.sqrt(6.0 / inSize)
lowVal = -1 * highVal
self.filtersValues[i] = np.random.uniform(low=lowVal, high=highVal, size=ksize)
def convLayersFeedForward(self, im):
self.convInputs = []
len_m = len(im.shape)
#print('len_m:', len_m)
for i, cl in enumerate(self.convFilters):
self.convInputs.append(im)
if (self.filtersValues[i] is None):
if (type(self.convFilterSizes) == list):
ks = self.convFilterSizes[i]
else:
ks = self.convFilterSizes
inSize = np.prod(im.shape)
if 'xavier' in self.kernel_initializer:
if self.batch_size == 1:
imshape = np.asarray([im.shape[0], im.shape[1]])
else:
imshape = np.asarray([im.shape[1], im.shape[2]])
extraShape = int((ks % 2) == 0)
ks2 = int(ks / 2) * 2
outSize = np.prod((imshape - ks2 + extraShape)) * cl
else:
outSize = 0
if i == 0 and len_m == 3:
if self.batch_size == 1:
self.kernelInitializer(i, (ks,ks,im.shape[2],cl), inSize, outSize)
else:
self.kernelInitializer(i, (ks,ks,cl), inSize, outSize)
else:
self.kernelInitializer(i, (ks,ks,cl), inSize, outSize)
k_filters = self.filtersValues[i]
if (type(self.convStride) == list):
stride_par = self.convStride[i]
else:
stride_par = self.convStride
#print('Convolutional layer', i, '\n')
#print('Layer input shape:', im.shape)
#print('Layer filters array shape:', k_filters.shape)
# Start of convolutions
#im = self.conv_filters(im, k_filters, relu=True, stride=stride_par, mode='valid')
filtersValues_shape01 = np.asarray([k_filters.shape[0], k_filters.shape[1]])
filtersValues_shape_d2 = (filtersValues_shape01 / 2).astype(int)
extraShape = (filtersValues_shape01 % 2) == 0
eS0 = extraShape[0].astype(int)
eS1 = extraShape[1].astype(int)
posYf = eS0
posXf = eS1
filter_shape0 = k_filters.shape[0]
filter_shape1 = k_filters.shape[1]
if (len(k_filters.shape) >= 3):
num_filters = k_filters.shape[-1]
else:
num_filters = 1
if self.batch_size == 1:
xshape = np.asarray([im.shape[0], im.shape[1]])
else:
xshape = np.asarray([im.shape[1], im.shape[2]])
output_shape = xshape - filtersValues_shape_d2*2 + eS0
if ((len(im.shape) < len(k_filters.shape)) or (len(im.shape) == 2 and num_filters == 1)):
Xr = np.expand_dims(im, axis=-1)
Xr = np.repeat(Xr, num_filters, axis=-1)
else:
if (len(im.shape) == len(k_filters.shape)):
if self.batch_size == 1:
new_filters = int(im.shape[-1] / num_filters)
Xr = np.repeat(im, new_filters, axis=-1)
else:
Xr = np.expand_dims(im, axis=-1)
Xr = np.repeat(Xr, num_filters, axis=-1)
else:
Xr = im
if (len(Xr.shape) == 2):
npad = ((0,eS0), (0,eS1))
out_s = [output_shape[0], output_shape[1], 1]
elif (len(Xr.shape) == 3):
npad = ((0,eS0), (0,eS1), (0,0))
out_s = [output_shape[0], output_shape[1], num_filters]
elif (len(Xr.shape) == 4):
if self.batch_size == 1:
npad = ((0,eS0), (0,eS1), (0,0), (0,0))
out_s = [output_shape[0], output_shape[1], im.shape[2], num_filters]
else:
npad = ((0,0), (0,eS0), (0,eS1), (0,0))
out_s = [im.shape[0], output_shape[0], output_shape[1], num_filters]
X_pad = np.pad(Xr, npad, 'constant')
out_s[0 if self.batch_size == 1 else 1] = int(np.ceil(out_s[0 if self.batch_size == 1 else 1] / stride_par))
out_s[1 if self.batch_size == 1 else 2] = int(np.ceil(out_s[1 if self.batch_size == 1 else 2] / stride_par))
conv_output = np.zeros(out_s)
if self.batch_size != 1:
k_filters = np.expand_dims(k_filters, axis=0)
k_filters = np.repeat(k_filters, im.shape[0], axis=0)
#print(Xr.shape, X_pad.shape, k_filters.shape, conv_output.shape, output_shape)
for posY in range(0, filter_shape0):
for posX in range(0, filter_shape1):
# valid convolution
if self.batch_size == 1:
conv_output += X_pad[posYf:posYf+output_shape[0]:stride_par, posXf:posXf+output_shape[1]:stride_par] * k_filters[posY, posX]
else:
conv_output += X_pad[:, posYf:posYf+output_shape[0]:stride_par, posXf:posXf+output_shape[1]:stride_par] * k_filters[:, posY, posX].reshape(k_filters.shape[0],1,1,k_filters.shape[3])
posXf = posXf + 1
posYf = posYf + 1
posXf = eS1
# End of convolutions
if self.pre_norm:
ax_f = tuple(range(0,len(conv_output.shape)))
if self.batch_size == 1:
ax_f = ax_f[0:-1]
conv_output = (conv_output - np.mean(conv_output, axis=ax_f)) / (np.std(conv_output, axis=ax_f) + 1e-7)
else:
ax_f = ax_f[1:-1]
conv_output = (conv_output - np.mean(conv_output, axis=ax_f).reshape(conv_output.shape[0],1,1,conv_output.shape[3])) / (np.std(conv_output, axis=ax_f).reshape(conv_output.shape[0],1,1,conv_output.shape[3]) + 1e-7)
#conv_output = (conv_output - conv_output.mean()) / (conv_output.std() + 1e-7)
im = self.ActivationFunction(conv_output, 'relu')
#print('Layer output shape:', im.shape, '\n---------------------\n')
return im
def convLayersBackpropagation(self, last_layer_output, prev_cost):
i = len(self.filtersValues) - 1
last_shape = list(last_layer_output.shape)
if self.batch_size != 1:
batch_el = last_shape[0]
last_shape = last_shape[1:] + [batch_el]
error_by_x = np.reshape(prev_cost, last_shape)
"""
if self.batch_size == 1:
num_filters = last_layer_output.shape[2]
else:
num_filters = last_layer_output.shape[3]
"""
self.log('Start of convLayersBackpropagation:', '\n')
#self.log('prev_cost:', prev_cost.shape, prev_cost, '\n')
#self.log('last_layer_output:', last_layer_output.shape, last_layer_output, '\n')
#self.log('error_by_x:', error_by_x.shape, error_by_x, '\n')
#if self.batch_size != 1:
#error_by_x = np.mean(error_by_x, axis=0)
for k_filters in self.filtersValues[::-1]:
X = self.convInputs[i]
if self.batch_size != 1:
X_batchshape = list(X.shape)
X_batch_elements = X_batchshape[0]
X_batchshape = X_batchshape[1:] + [X_batch_elements]
X = np.reshape(X, X_batchshape)
#X = np.mean(X, axis=0)
# to dilate gradient if needed because of stride
if (type(self.convStride) == list):
stride_par = self.convStride[i]
else:
stride_par = self.convStride
if stride_par != 1:
#erShape = error_by_x.shape[0] * stride_par
erShape = (X.shape[0])
if self.batch_size == 1:
error_by_output = np.zeros((erShape, erShape, self.convFilters[i]), dtype=float)
else:
error_by_output = np.zeros((erShape, erShape, self.convFilters[i], batch_el), dtype=float)
#print(error_by_output.shape, error_by_x.shape)
posI = 0
posJ = 0
erx1 = (error_by_x.shape[0])
erx2 = (error_by_x.shape[1])
# Zero-interweave:
for pe_i in range(0, erx1):
for pe_j in range(0, erx2):
error_by_output[posI, posJ] = error_by_x[pe_i, pe_j]
if (posJ + 2) < erShape:
posJ = posJ + 2
else:
posJ = posJ + 1
if (posI + 2) < erShape:
posI = posI + 2
else:
posI = posI + 1
posJ = 0
else:
# dE/dO
error_by_output = error_by_x
f_rotated = np.flip(self.filtersValues[i], 0)
f_rotated = np.flip(f_rotated, 1)
# dE/dF
#error_by_filter = self.conv_filters(X, error_by_output, relu=False, stride=1, mode='valid')
# dE/dX
#error_by_x = self.conv_filters(f_rotated, error_by_output, relu=False, stride=1, mode='full')
# Start of convolutions
err_output_shape01 = np.asarray([error_by_output.shape[0], error_by_output.shape[1]])
err_out_shape_d2 = (err_output_shape01 / 2).astype(int)
xshape = np.asarray([X.shape[0], X.shape[1]])
fshape = np.asarray([f_rotated.shape[0], f_rotated.shape[1]])
extraShape = (err_output_shape01 % 2) == 0
eS0 = extraShape[0].astype(int)
eS1 = extraShape[1].astype(int)
err_filt_shape = xshape - err_out_shape_d2*2 + eS0
err_x_shape = fshape + err_out_shape_d2*2 + eS0
num_filters = self.filtersValues[i].shape[-1]
#print(error_by_output.shape, xshape, err_output_shape01, err_out_shape_d2*2, eS0, err_filt_shape)
if self.batch_size == 1:
error_by_filter = np.zeros((err_filt_shape[0], err_filt_shape[1], num_filters))
error_by_x = np.zeros((err_x_shape[0], err_x_shape[1], num_filters))
else:
error_by_filter = np.zeros((err_filt_shape[0], err_filt_shape[1], num_filters, X_batch_elements))
error_by_x = np.zeros((err_x_shape[0], err_x_shape[1], num_filters, X_batch_elements))
err_out_shape0 = error_by_output.shape[0]
err_out_shape1 = error_by_output.shape[1]
fil_shape0 = error_by_filter.shape[0]
fil_shape1 = error_by_filter.shape[1]
ex_shape0 = self.filtersValues[i].shape[0]
ex_shape1 = self.filtersValues[i].shape[1]
posYf = eS0
posXf = eS1
if (len(X.shape) < 3):
Xr = np.expand_dims(X, axis=-1)
Xr = np.repeat(Xr, num_filters, axis=-1)
else:
Xr = X
if (len(Xr.shape) == 3):
X_pad = np.pad(Xr, ((0,eS0), (0,eS1), (0,0)), 'constant')
elif (len(Xr.shape) == 4):
X_pad = np.pad(Xr, ((0,eS0), (0,eS1), (0,0), (0,0)), 'constant')
else: # color image with batch
X_pad = np.pad(Xr, ((0,0), (0,eS0), (0,eS1), (0,0), (0,0)), 'constant')
layer_filters = self.filtersValues[i]
if self.batch_size != 1:
layer_filters = np.expand_dims(layer_filters, axis=-1)
layer_filters = np.repeat(layer_filters, X_batch_elements, axis=-1)
#print(X_pad.shape, error_by_output.shape, error_by_filter.shape, self.filtersValues[i].shape, error_by_output.shape, error_by_x.shape)
for posY in range(0, err_out_shape0):
for posX in range(0, err_out_shape1):
# valid convolution (dE/dF)
error_by_filter += X_pad[posYf:posYf+fil_shape0, posXf:posXf+fil_shape1] * error_by_output[posY, posX]
# full convolution (dE/dX)
error_by_x[posYf:posYf+ex_shape0, posXf:posXf+ex_shape1] += layer_filters * error_by_output[posY, posX]
posXf = posXf + 1
posYf = posYf + 1
posXf = eS1
error_by_x = np.flip(error_by_x, 0)
error_by_x = np.flip(error_by_x, 1)
# End of convolutions
#print(X.shape, X_pad.shape, self.filtersValues[i].shape, error_by_filter.shape, error_by_x.shape, error_by_output.shape)
#self.log('error_by_filter:', error_by_filter[:,:,0], '\n\n')
#self.log('prev filtersValues[i]:', self.filtersValues[i][:,:,0], '\n\n')
#self.log('error_by_x:', error_by_x[:,:,0], '\n\n')
if self.batch_size != 1:
error_by_filter = np.mean(error_by_filter, axis=-1)
#if self.pre_norm:
#ax_f = tuple(range(0,len(error_by_filter[i].shape)))[0:-1]
#error_by_filter = (error_by_filter - np.mean(error_by_filter, axis=ax_f)) / (np.std(error_by_filter, axis=ax_f) + 1e-7)
#error_by_filter = (error_by_filter - error_by_filter.mean()) / (error_by_filter.std() + 1e-7)
# Filters update
self.filtersValues[i] = self.filtersValues[i] - self.learningRateConv * error_by_filter
if self.pre_norm:
ax_f = tuple(range(0,len(self.filtersValues[i].shape)))[0:-1]
self.filtersValues[i] = (self.filtersValues[i] - np.mean(self.filtersValues[i], axis=ax_f)) / (np.std(self.filtersValues[i], axis=ax_f) + 1e-7)
#self.log('filtersValues[i] updated:', self.filtersValues[i][:,:,0], '\n\n')
#self.log('\n-----------------------\n')
i = i - 1
self.log('End of convLayersBackpropagation')
def draw(self, showWeights=False, textSize=9, customRadius=0, showLegend=True):
fig = plt.figure(figsize=(10,8))
ax = fig.subplots()
ax.set_title("Layers and neurons of the fully-connected part of the ConvNet")
ax.set_xlim(xmin=0, xmax=1)
ax.set_ylim(ymin=0, ymax=1)
xmin, xmax, ymin, ymax = ax.axis()
xdim = xmax - xmin
ydim = ymax - ymin
space_per_layer = xdim / (len(self.hiddenL) + 1)
x0 = xmin
x1 = xmin + space_per_layer
medio_intervalo = space_per_layer / 2
if customRadius <= 0:
radio = 1 / ((sum(self.hiddenL) + self.n_layer0) * 5)
else:
radio = customRadius
lista_lineas_xy = []
lasth = self.n_layer0
num_FC_layers = len(self.hiddenL) + 1
# For each layer
for capa, h in enumerate([self.n_layer0] + self.hiddenL):
space_per_neuron = ydim / h
y0 = ymin
y1 = ymin + space_per_neuron
medio_intervalo_n = space_per_neuron / 2
lista_lineas_xy_pre = []
ne = (lasth * h) - 1
neY = h - 1
if showLegend:
if capa == 0:
plot_label = "First FC layer"
neuron_color = 'r'
elif capa + 1 == num_FC_layers:
plot_label = "Output layer"
neuron_color = 'b'
else:
plot_label = "Hidden FC layer"
neuron_color = 'g'
if capa > 1:
plot_label = "_" + plot_label # Avoid displaying the same label in the legend for each hidden layer
else:
plot_label = ""
neuron_color = 'r'
# For each neuron in this layer
for j in range(0, h):
plot_label = plot_label if j == 0 else ("_" + plot_label) # Avoid displaying the same label in the legend for each neuron in that layer
ax.add_patch(plt.Circle(((medio_intervalo + x0), (medio_intervalo_n + y0)), radio, color=neuron_color, label=plot_label, zorder=1))
neX = lasth - 1
# For each input to this neuron
for xy in lista_lineas_xy:
ax.plot([xy[0],(medio_intervalo + x0)],[xy[1], (medio_intervalo_n + y0)], zorder=0)
my = ((medio_intervalo_n + y0) - xy[1])
mx = ((medio_intervalo + x0) - xy[0])
pendiente = my / mx
ordenada_origen = xy[1] - pendiente * xy[0]
margen_ord = 0.015
if pendiente < 0:
margen_ord = -0.045 # compensate text rotation
ordenada_origen = ordenada_origen + margen_ord # add the text above the line
# random between the x's of the line segment (minus a margin so it does not appear too close to the neuron)
mx2 = random.uniform(xy[0] + 0.04, (medio_intervalo + x0) - 0.04)
my2 = pendiente*mx2 + ordenada_origen
alfa = math.degrees(math.atan(pendiente))
if showWeights:
ax.text(mx2, my2, round(self.hiddenWeights[capa-1][neX][neY], 3), rotation=alfa, fontsize=textSize, zorder=2)
ne = ne - 1
neX = neX - 1 # Index of the neuron of the previous layer
lista_lineas_xy_pre.append([(medio_intervalo + x0), (medio_intervalo_n + y0)])
neY = neY - 1 # Index of the neuron of the current layer
y0 = y0 + space_per_neuron
y1 = y1 + space_per_neuron
lasth = h
x0 = x0 + space_per_layer
x1 = x1 + space_per_layer
lista_lineas_xy = lista_lineas_xy_pre
if showLegend:
plt.legend(loc='best')
plt.show()
def importModel(self, path='', filename='ConvNetAbel_model'):
self.hiddenWeights = np.load(path + filename + '_weights.npy', allow_pickle=True)
mConfig = np.load(path + filename + '_config.npy', allow_pickle=True)
self.n_layer0 = int(mConfig[0])
self.showLogs = bool(mConfig[1])
self.lastLayerNeurons = int(mConfig[2])
self.numEpochs = int(mConfig[3])
self.learningRate = float(mConfig[4])
self.debugMode = int(mConfig[5])
self.softmax = bool(mConfig[6])
self.activationFunction = str(mConfig[7])
self.verbose = bool(mConfig[8])
self.use = str(mConfig[9])
self.batch_size = int(mConfig[10])
self.batch_gradient = str(mConfig[11])
self.batch_mult = int(mConfig[12])
self.dropout = float(mConfig[13])
self.pre_norm = bool(mConfig[14])
self.shuffle = bool(mConfig[15])
self.iterationDrop = float(mConfig[16])
self.version_importedModel = mConfig[17]
self.hiddenL2 = mConfig[18]
self.hiddenL = mConfig[19]
convConfig = np.load(path + filename + '_convConfig.npy', allow_pickle=True)
self.convFilters = convConfig[0]
self.convStride = convConfig[1]
self.convFilterSizes = convConfig[2]
self.kernel_initializer = str(convConfig[3])
self.convEpochs = int(convConfig[4])
self.learningRateConv = float(convConfig[5])
self.filtersValues = np.load(path + filename + '_filtersValues.npy', allow_pickle=True)
if self.debugMode > 0:
self.meanCostByEpoch = np.load(path + filename + '_meanCostByEpoch.npy', allow_pickle=True).tolist()
if self.debugMode > 1:
self.debugWeights = np.load(path + filename + '_debugWeights.npy', allow_pickle=True).tolist()
def exportModel(self, path='', filename='ConvNetAbel_model'):
np.save(path + filename + '_weights.npy', np.asarray(self.hiddenWeights, dtype=object))
mConfig = []
mConfig.append(self.n_layer0)
mConfig.append(self.showLogs)
mConfig.append(self.lastLayerNeurons)
mConfig.append(self.numEpochs)
mConfig.append(self.learningRate)
mConfig.append(self.debugMode)
mConfig.append(self.softmax)
mConfig.append(self.activationFunction)
mConfig.append(self.verbose)
mConfig.append(self.use)
mConfig.append(self.batch_size)
mConfig.append(self.batch_gradient)
mConfig.append(self.batch_mult)
mConfig.append(self.dropout)
mConfig.append(self.pre_norm)
mConfig.append(self.shuffle)
mConfig.append(self.iterationDrop)
mConfig.append(self.version)
mConfig.append(self.hiddenL2)
mConfig.append(self.hiddenL)
mConfig = np.asarray(mConfig, dtype=object)
np.save(path + filename + '_config.npy', mConfig)
convConfig = []
convConfig.append(self.convFilters)
convConfig.append(self.convStride)
convConfig.append(self.convFilterSizes)
convConfig.append(self.kernel_initializer)
convConfig.append(self.convEpochs)
convConfig.append(self.learningRateConv)
convConfig = np.asarray(convConfig, dtype=object)
np.save(path + filename + '_convConfig.npy', convConfig)
np.save(path + filename + '_filtersValues.npy', np.asarray(self.filtersValues, dtype=np.float32))
if self.debugMode > 0:
np.save(path + filename + '_meanCostByEpoch.npy', self.meanCostByEpoch)
if self.debugMode > 1:
np.save(path + filename + '_debugWeights.npy', np.asarray(self.debugWeights, dtype=object))
def log(self, *m):
if self.showLogs:
print(*m)
def printVerbose(self, *m):
if self.verbose:
print(*m)
def initializeWeight(self, n, i, lastN):
if len(self.manualWeights) == 0:
numW = n * lastN
if self.rangeRandomWeight is None:
if self.activationFunction == 'sigmoid':
if self.XavierInitialization == 'normalized': # Normalized Xavier initialization
highVal = math.sqrt(6.0) / math.sqrt(lastN + n)
lowVal = -1 * highVal
mnar = np.random.uniform(low=lowVal, high=highVal, size=(numW,1))
else: # Xavier initialization
mnar = np.random.randn(numW, 1) * math.sqrt(1.0 / lastN)
else:
mnar = np.random.randn(numW, 1) * math.sqrt(2.0 / lastN) # He initialization
else:
highVal = self.rangeRandomWeight[1]
lowVal = self.rangeRandomWeight[0]
mnar = np.random.uniform(low=lowVal, high=highVal, size=(numW,1))
else:
mnar = np.asarray(self.manualWeights[i])
#mnar = mnar.reshape(mnar.shape[0], 1)
#ns = int(mnar.shape[0] / lastN)
#print('ns: ', ns)
mnar = mnar.reshape(lastN, n, order='F')
return mnar
def ActivationFunction(self, x, activ_type='sigmoid'):
if activ_type=='sigmoid':
return 1.0/(1 + np.exp(-1*x))
elif activ_type=='relu':
return np.where(x > 0, x, 0)
elif activ_type=='softplus':
return np.log(1 + np.exp(x))
elif activ_type=='leakyrelu':
return np.where(x > 0, x, 0.01 * x)
elif activ_type=='identity':
return np.copy(x)
else:
x[x>0.5] = 1
x[x<=0.5] = 0
return x
def functionDerivative(self, x, activ_type='sigmoid'):
if activ_type=='sigmoid':
return self.ActivationFunction(x,activ_type) * (1-self.ActivationFunction(x,activ_type))
elif activ_type=='relu':
return np.where(x >= 0, 1, 0)
elif activ_type=='softplus':
return 1.0/(1 + np.exp(-1*x))
elif activ_type=='leakyrelu':
return np.where(x >= 0, 1, 0.01)
elif activ_type=='identity':
return 1
else:
return 1
def cost(self, y_true, y_pred):
c = y_true - y_pred
return c
def softmaxF(self, x):
if np.max(np.abs(x)) < 500: # prevent overflow
expX = np.exp(x)