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I'm trying to read numbers from a picture with pytesseract.
This is my code:
import matplotlib.pyplot as plt
import pytesseract
img = cv2.imread(r'bild4.jpg', 2)
[ret, bw_img] = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY)
imgplot = plt.imshow(bw_img)
plt.show()
text = pytesseract.image_to_string(bw_img, config='digits')
print("Text: " + text)
I tried many ways to preprocess the image, the best I got is:
"673
504 .
5 552"
Only the last line is correct.
Without the config='digits' I get:
"673 ost
Fir 504 .
5 552
ii"
I tried with black and withe only, witch is really easy to read for me as a human, but it don't recognice numbers at all...
You could use inRange thresholding
Read the image and convert it to the HSV color-scale
bgr = cv2.imread("JSe5v.jpg")
hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
Initialize mask and kernel
mask = cv2.inRange(hsv, np.array([0, 0, 244]), np.array([179, 35, 255]))
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
Remove the background using mask and kernel
dilated = cv2.dilate(mask, kernel, iterations=1)
thresh = cv2.bitwise_and(dilated, mask)
The result will be:
If you read it with psm mode 6
675 OS!
312504
5 552
1st line second part is not correct "51" is recognized as "S!"
You could look for improving the tesseract accuracy
Code:
import cv2
import pytesseract
import numpy as np
bgr = cv2.imread("JSe5v.jpg")
hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array([0, 0, 244]), np.array([179, 35, 255]))
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
dilated = cv2.dilate(mask, kernel, iterations=1)
thresh = cv2.bitwise_and(dilated, mask)
text = pytesseract.image_to_string(thresh, config="--psm 6")
print(text)
I'm building a convolutional autoencoder, but want the encoding to be in a linear form so I can more easily feed it as input into an MLP. I have two convolutional layers on the encoder along with a linear inner layer to reduce dimension. This encoding is then fed into the corresponding decoder.
When I flatten the output of the second convolutional layer, based on my calculation (using the standard formula: Calculate the Output size in Convolution layer) should come out to a 1x100352 rank 1 tensor. However, when I set the input dimension of the linear layer to be 100352, the flattened rank 1 tensor has dimension 1x50176. Then comes the weird part.
I tried changing the input dimension of the linear layer to be 50176, assuming I had miscalculated. When I do this, the reshaped rank 1 tensor confusingly becomes 1x100352, and then the aforementioned weight matrix becomes 50176x256 as expected.
This response to modifying the linear layer's input dimension doesn't make sense to me. That hyperparameter controls the weight matrix correctly, but I guess I'm uncertain why it has any bearing on the linear layer's input since that's just a reshaped tensor output from a convolutional layer whose hyperparameters are unrelated to the hyperparameter in question.
I apologize if I'm just missing something obvious. I'm very new to pytorch, and I couldn't find any other posts which discussed this sort of issue.
Here's what I believe to be the minimal reproducible example:
import os
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets
from torch.utils.data import DataLoader
from torchvision.utils import save_image
class convAutoEncoder(nn.Module):
def __init__(self,**kwargs):
super().__init__()
#Creating network structure
#Encoder portion of autoencoder
self.enc1 = nn.Conv2d(in_channels = kwargs["inputChannels"], out_channels = kwargs["channelsEncoderMid"], kernel_size = kwargs["kernelSize"])
self.enc2 = nn.Conv2d(in_channels = kwargs["channelsEncoderMid"], out_channels = kwargs["channelsEncoderInner"], kernel_size = kwargs["kernelSize"])
self.enc3 = nn.Linear(in_features = kwargs["intoLinear"], out_features = kwargs["linearEncoded"])
#Decoder portion of autoencoder
self.dec1 = nn.Linear(in_features = kwargs["linearEncoded"], out_features = kwargs["intoLinear"])
self.dec2 = nn.ConvTranspose2d(in_channels = kwargs["channelsEncoderInner"], out_channels = kwargs["channelsDecoderMid"], kernel_size = kwargs["kernelSize"])
self.dec3 = nn.ConvTranspose2d(in_channels = kwargs["channelsDecoderMid"], out_channels = kwargs["inputChannels"], kernel_size = kwargs["kernelSize"])
def forward(self,x):
#Encoding
x = F.relu(self.enc1(x))
x = F.relu(self.enc2(x))
x = x.reshape(1,-1)
x = x.squeeze()
x = F.relu(self.enc3(x))
#Decoding
x = F.relu(self.dec1(x))
x = x.reshape([32,4,28,28])
x = F.relu(self.dec2(x))
x = F.relu(self.dec3(x))
return x
def encodeDecodeConv(numEpochs = 20, input_Channels = 3, batchSize = 32,
channels_Encoder_Inner = 4, channels_Encoder_Mid = 8, into_Linear = 100352,
linear_Encoded = 256, channels_Decoder_Mid = 8, kernel_Size = 3,
learningRate = 1e-3):
#Pick a device. If GPU available, use that. Otherwise, use CPU.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#Define data transforms
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
#Define training dataset
trainSet = datasets.CIFAR10(root = './data', train = True, download = True, transform = transform)
#Define testing dataset
testSet = datasets.CIFAR10(root = './data', train = False, download = True, transform = transform)
#Define data loaders
trainLoader = DataLoader(trainSet, batch_size = batchSize, shuffle = True)
testLoader = DataLoader(testSet, batch_size = batchSize, shuffle = True)
#Initialize neural network
model = convAutoEncoder(inputChannels = input_Channels, channelsEncoderMid = channels_Encoder_Mid, channelsEncoderInner = channels_Encoder_Inner, intoLinear = into_Linear, linearEncoded = linear_Encoded, channelsDecoderMid = channels_Decoder_Mid, kernelSize = kernel_Size)
#Optimization setup
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(),lr = learningRate)
lossTracker = []
for epoch in range(numEpochs):
loss = 0
for data,_ in trainLoader:
data = data.to(device)
optimizer.zero_grad()
outputs = model(data)
train_loss = criterion(outputs,data)
train_loss.backward()
optimizer.step()
loss += train_loss.item()
loss = loss/len(trainLoader)
print('Epoch {} of {}, Train loss: {:.3f}'.format(epoch+1,numEpochs,loss))
encodeDecodeConv()
Edit2: Somewhere in the CIFAR10 dataset, the data appears to change dimension. After playing around with print statements more, I discovered that setting the relevant hyperparameter to 100352 works great for many entries, but then seemingly one image pops up that has a different size. Not sure why that would occur, though.
I'm currently trying to use some images from the Sun dataset, with varying shapes, around (1000, 400, 1). Since they are varying in shape, my approach to it was to create a numpy array with numpy arrays in it, so that I dont have to define any shape of it. What I want to do is to train a basic CNN using these pictures. The problem is, I dont think my CNN understands how my input data is defined really. In my implementation, self.X_train[0] for example contains one image (with correspondsing target in self.Y_train[0] and so on). My code right now is looking like:
import os
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten
class network:
def __init__(self):
self.X_train, self.Y_train = self.generate_targets()
def generate_targets(self):
path = 'C:\\Users\\joaki\\PycharmProjects\\project\\project dl\\'
folder = os.fsencode(path)
targets = []
inputs = []
for file in os.listdir(folder):
filename = os.fsdecode(file)
if filename.endswith(('.jpg')):
img = Image.open(filename).convert('RGB')
img2 = Image.open(filename).convert('L')
arr2 = np.array(img2)
arr2 = arr2.reshape((arr2.shape[0], arr2.shape[1], 1))
inputs.append(arr2)
arr = np.array(img)
targets.append(arr)
Y = np.array(targets)
X = np.array(inputs)
return X, Y
def plotting(self, type):
plt.figure(figsize=(20, 10))
for i in range(self.X_train.shape[0]):
plt.subplot(2, 2, i+1)
if type == 'targets':
lum_img = self.Y_train[i][:, :, :] #[:,:,:] för färg
plt.imshow(lum_img)
if type == 'inputs':
lum_img = self.X_train[i][:, :, 0] # [:,:,:] för färg
plt.imshow(lum_img)
plt.show()
def train_network(self):
model = Sequential()
# add model layers
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape = (None, None, 1)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(self.X_train, self.Y_train, batch_size = 1, validation_data=(self.X_train, self.Y_train), epochs=3)
network1 = network()
#network1.plotting('inputs')
network1.train_network()
#print(network1.X_train[0].shape)
Is there solution to this problem, if so, can someone provide information or a source that I should follow? Thanks in advance!
I am currently trying to get a decent score (> 40% accuracy) with Keras on CIFAR 100. However, I'm experiencing a weird behaviour of a CNN model: It tends to predict some classes (2 - 5) much more often than others:
The pixel at position (i, j) contains the count how many elements of the validation set from class i were predicted to be of class j. Thus the diagonal contains the correct classifications, everything else is an error. The two vertical bars indicate that the model often predicts those classes, although it is not the case.
CIFAR 100 is perfectly balanced: All 100 classes have 500 training samples.
Why does the model tend to predict some classes MUCH more often than other classes? How can this be fixed?
The code
Running this takes a while.
#!/usr/bin/env python
from __future__ import print_function
from keras.datasets import cifar100
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np
batch_size = 32
nb_classes = 100
nb_epoch = 50
data_augmentation = True
# input image dimensions
img_rows, img_cols = 32, 32
# The CIFAR10 images are RGB.
img_channels = 3
# The data, shuffled and split between train and test sets:
(X, y), (X_test, y_test) = cifar100.load_data()
X_train, X_val, y_train, y_val = train_test_split(X, y,
test_size=0.20,
random_state=42)
# Shuffle training data
perm = np.arange(len(X_train))
np.random.shuffle(perm)
X_train = X_train[perm]
y_train = y_train[perm]
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_val.shape[0], 'validation samples')
print(X_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
Y_val = np_utils.to_categorical(y_val, nb_classes)
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
X_train = X_train.astype('float32')
X_val = X_val.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_val /= 255
X_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_val, y_val),
shuffle=True)
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(X_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_val, Y_val))
model.save('cifar100.h5')
Visualization code
#!/usr/bin/env python
"""Analyze a cifar100 keras model."""
from keras.models import load_model
from keras.datasets import cifar100
from sklearn.model_selection import train_test_split
import numpy as np
import json
import io
import matplotlib.pyplot as plt
try:
to_unicode = unicode
except NameError:
to_unicode = str
n_classes = 100
def plot_cm(cm, zero_diagonal=False):
"""Plot a confusion matrix."""
n = len(cm)
size = int(n / 4.)
fig = plt.figure(figsize=(size, size), dpi=80, )
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(cm), cmap=plt.cm.viridis,
interpolation='nearest')
width, height = cm.shape
fig.colorbar(res)
plt.savefig('confusion_matrix.png', format='png')
# Load model
model = load_model('cifar100.h5')
# Load validation data
(X, y), (X_test, y_test) = cifar100.load_data()
X_train, X_val, y_train, y_val = train_test_split(X, y,
test_size=0.20,
random_state=42)
# Calculate confusion matrix
y_val_i = y_val.flatten()
y_val_pred = model.predict(X_val)
y_val_pred_i = y_val_pred.argmax(1)
cm = np.zeros((n_classes, n_classes), dtype=np.int)
for i, j in zip(y_val_i, y_val_pred_i):
cm[i][j] += 1
acc = sum([cm[i][i] for i in range(100)]) / float(cm.sum())
print("Validation accuracy: %0.4f" % acc)
# Create plot
plot_cm(cm)
# Serialize confusion matrix
with io.open('cm.json', 'w', encoding='utf8') as outfile:
str_ = json.dumps(cm.tolist(),
indent=4, sort_keys=True,
separators=(',', ':'), ensure_ascii=False)
outfile.write(to_unicode(str_))
Red herrings
tanh
I've replaced tanh by relu. The history csv looks ok, but the visualization has the same problem:
Please also note that the validation accuracy here is only 3.44%.
Dropout + tanh + border mode
Removing dropout, replacing tanh by relu, setting border mode to same everywhere: history csv
The visualization code still gives a much lower accuracy (8.50% this time) than the keras training code.
Q & A
The following is a summary of the comments:
The data is evenly distributed over the classes. So there is no "over training" of those two classes.
Data augmentation is used, but without data augmentation the problem persists.
The visualization is not the problem.
If you get good accuracy during training and validation, but not when testing, make sure you do exactly the same preprocessing on your dataset in both cases.
Here you have when training:
X_train /= 255
X_val /= 255
X_test /= 255
But no such code when predicting for your confusion matrix. Adding to testing:
X_val /= 255.
Gives the following nice looking confusion matrix:
I don't have a good feeling with this part of the code:
model.add(Dense(1024))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
The remaining model is full of relus, but here there is a tanh.
tanh sometimes vanishes or explodes (saturates at -1 and 1), which might lead to your 2-class overimportance.
keras-example cifar 10 basically uses the same architecture (dense-layer sizes might be different), but also uses a relu there (no tanh at all). The same goes for this external keras-based cifar 100 code.
One important part of the problem was that my ~/.keras/keras.json was
{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}
Hence I had to change image_dim_ordering to tf. This leads to
and an accuracy of 12.73%. Obviously, there is still a problem as the validation history gave 45.1% accuracy.
I don't see you doing mean-centering, even in datagen. I suspect this is the main cause. To do mean centering using ImageDataGenerator, set featurewise_center = 1. Another way is to subtract the ImageNet mean from each RGB pixel. The mean vector to be subtracted is [103.939, 116.779, 123.68].
Make all activations relus, unless you have a specific reason to have a single tanh.
Remove two dropouts of 0.25 and see what happens. If you want to apply dropouts to convolution layer, it is better to use SpatialDropout2D. It is somehow removed from Keras online documentation but you can find it in the source.
You have two conv layers with same and two with valid. There is nothing wrong in this, but it would be simpler to keep all conv layers with same and control your size just based on max-poolings.
Autoencoder networks seems to be way trickier than normal classifier MLP networks. After several attempts using Lasagne all what I get in the reconstructed output is something that resembles at its best a blurry averaging of all the images of the MNIST database without distinction on what the input digit actually is.
The networks structure I chose are the following cascade layers:
input layer (28x28)
2D convolutional layer, filter size 7x7
Max Pooling layer, size 3x3, stride 2x2
Dense (fully connected) flattening layer, 10 units (this is the bottleneck)
Dense (fully connected) layer, 121 units
Reshaping layer to 11x11
2D convolutional layer, filter size 3x3
2D Upscaling layer factor 2
2D convolutional layer, filter size 3x3
2D Upscaling layer factor 2
2D convolutional layer, filter size 5x5
Feature max pooling (from 31x28x28 to 28x28)
All the 2D convolutional layers have the biases untied, sigmoid activations and 31 filters.
All the fully connected layers have sigmoid activations.
The loss function used is squared error, the updating function is adagrad. The length of the chunk for the learning is 100 samples, multiplied for 1000 epochs.
Just for completeness, the following is the code I used:
import theano.tensor as T
import theano
import sys
sys.path.insert(0,'./Lasagne') # local checkout of Lasagne
import lasagne
from theano import pp
from theano import function
import gzip
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
def load_mnist():
def load_mnist_images(filename):
with gzip.open(filename, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=16)
# The inputs are vectors now, we reshape them to monochrome 2D images,
# following the shape convention: (examples, channels, rows, columns)
data = data.reshape(-1, 1, 28, 28)
# The inputs come as bytes, we convert them to float32 in range [0,1].
# (Actually to range [0, 255/256], for compatibility to the version
# provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)
return data / np.float32(256)
def load_mnist_labels(filename):
# Read the labels in Yann LeCun's binary format.
with gzip.open(filename, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=8)
# The labels are vectors of integers now, that's exactly what we want.
return data
X_train = load_mnist_images('train-images-idx3-ubyte.gz')
y_train = load_mnist_labels('train-labels-idx1-ubyte.gz')
X_test = load_mnist_images('t10k-images-idx3-ubyte.gz')
y_test = load_mnist_labels('t10k-labels-idx1-ubyte.gz')
return X_train, y_train, X_test, y_test
def plot_filters(conv_layer):
W = conv_layer.get_params()[0]
W_fn = theano.function([],W)
params = W_fn()
ks = np.squeeze(params)
kstack = np.vstack(ks)
plt.imshow(kstack,interpolation='none')
plt.show()
def main():
#theano.config.exception_verbosity="high"
#theano.config.optimizer='None'
X_train, y_train, X_test, y_test = load_mnist()
ohe = OneHotEncoder()
y_train = ohe.fit_transform(np.expand_dims(y_train,1)).toarray()
chunk_len = 100
visamount = 10
num_epochs = 1000
num_filters=31
dropout_p=.0
print "X_train.shape",X_train.shape,"y_train.shape",y_train.shape
input_var = T.tensor4('X')
output_var = T.tensor4('X')
conv_nonlinearity = lasagne.nonlinearities.sigmoid
net = lasagne.layers.InputLayer((chunk_len,1,28,28), input_var)
conv1 = net = lasagne.layers.Conv2DLayer(net,num_filters,(7,7),nonlinearity=conv_nonlinearity,untie_biases=True)
net = lasagne.layers.MaxPool2DLayer(net,(3,3),stride=(2,2))
net = lasagne.layers.DropoutLayer(net,p=dropout_p)
#conv2_layer = lasagne.layers.Conv2DLayer(dropout_layer,num_filters,(3,3),nonlinearity=conv_nonlinearity)
#pool2_layer = lasagne.layers.MaxPool2DLayer(conv2_layer,(3,3),stride=(2,2))
net = lasagne.layers.DenseLayer(net,10,nonlinearity=lasagne.nonlinearities.sigmoid)
#augment_layer1 = lasagne.layers.DenseLayer(reduction_layer,33,nonlinearity=lasagne.nonlinearities.sigmoid)
net = lasagne.layers.DenseLayer(net,121,nonlinearity=lasagne.nonlinearities.sigmoid)
net = lasagne.layers.ReshapeLayer(net,(chunk_len,1,11,11))
net = lasagne.layers.Conv2DLayer(net,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
net = lasagne.layers.Upscale2DLayer(net,2)
net = lasagne.layers.Conv2DLayer(net,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
#pool_after0 = lasagne.layers.MaxPool2DLayer(conv_after1,(3,3),stride=(2,2))
net = lasagne.layers.Upscale2DLayer(net,2)
net = lasagne.layers.DropoutLayer(net,p=dropout_p)
#conv_after2 = lasagne.layers.Conv2DLayer(upscale_layer1,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
#pool_after1 = lasagne.layers.MaxPool2DLayer(conv_after2,(3,3),stride=(1,1))
#upscale_layer2 = lasagne.layers.Upscale2DLayer(pool_after1,4)
net = lasagne.layers.Conv2DLayer(net,num_filters,(5,5),nonlinearity=conv_nonlinearity,untie_biases=True)
net = lasagne.layers.FeaturePoolLayer(net,num_filters,pool_function=theano.tensor.max)
print "output_shape:",lasagne.layers.get_output_shape(net)
params = lasagne.layers.get_all_params(net, trainable=True)
prediction = lasagne.layers.get_output(net)
loss = lasagne.objectives.squared_error(prediction, output_var)
#loss = lasagne.objectives.binary_crossentropy(prediction, output_var)
aggregated_loss = lasagne.objectives.aggregate(loss)
updates = lasagne.updates.adagrad(aggregated_loss,params)
train_fn = theano.function([input_var, output_var], loss, updates=updates)
test_prediction = lasagne.layers.get_output(net, deterministic=True)
predict_fn = theano.function([input_var], test_prediction)
print "starting training..."
for epoch in range(num_epochs):
selected = list(set(np.random.random_integers(0,59999,chunk_len*4)))[:chunk_len]
X_train_sub = X_train[selected,:]
_loss = train_fn(X_train_sub, X_train_sub)
print("Epoch %d: Loss %g" % (epoch + 1, np.sum(_loss) / len(X_train)))
"""
chunk = X_train[0:chunk_len,:,:,:]
result = predict_fn(chunk)
vis1 = np.hstack([chunk[j,0,:,:] for j in range(visamount)])
vis2 = np.hstack([result[j,0,:,:] for j in range(visamount)])
plt.imshow(np.vstack([vis1,vis2]))
plt.show()
"""
print "done."
chunk = X_train[0:chunk_len,:,:,:]
result = predict_fn(chunk)
print "chunk.shape",chunk.shape
print "result.shape",result.shape
plot_filters(conv1)
for i in range(chunk_len/visamount):
vis1 = np.hstack([chunk[i*visamount+j,0,:,:] for j in range(visamount)])
vis2 = np.hstack([result[i*visamount+j,0,:,:] for j in range(visamount)])
plt.imshow(np.vstack([vis1,vis2]))
plt.show()
import ipdb; ipdb.set_trace()
if __name__ == "__main__":
main()
Any ideas on how to improve this network to get a reasonably functioning autoencoder?