# net.rnn(text.max_word_length) net.regression(dimensions=2) # for def denseConv(net): # type: (layer.net) -> None print("Building dense-net") net.reshape(shape=[-1, size, size, letter.color_channels]) # Reshape input picture net.buildDenseConv(nBlocks=1) """ Baseline tests to see that your model doesn't have any bugs and can learn small test sites without efforts """ # net = layer.net(layer.baseline, input_width=size, output_width=nClasses, learning_rate=learning_rate) # net.train(data=data, test_step=1000) # run """ here comes the real network """ # net = layer.net(denseConv, input_width=size, output_width=2, learning_rate=learning_rate) net = layer.net(startPositionGanglion, input_width=size, output_width=2, learning_rate=learning_rate) # net.train(data=data,steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(data=data, steps=training_steps,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, dropout=.6, display_step=5, test_step=100) # run resume # net.predict() # nil=random # net.generate(3) # nil=random
from os import system import layer app = Tkinter.Tk() import matplotlib.pyplot as plt plt.matshow([[1, 0], [0, 1]], fignum=1) # print(dir(plt)) # help(plt) # ax.patch.set_facecolor('None') or ax.patch.set_visible(False). plt.draw() system('''/usr/bin/osascript -e 'tell app "Finder" to set frontmost of process "Python" to true' ''') # LOAD MODEL! net = layer.net(model="denseConv", input_shape=[28,28]) # net = layer.net(model="denseConv", input_shape=[784]) net.predict()#random : debug i = 0 width = 256 height = 256 def get_mouse_position(): if sys.platform == 'Windows': import win32api x, y = win32api.GetCursorPos() else: x, y = app.winfo_pointerxy() return x,y
def recurrent(net): # type: (layer.net) -> None net.rnn() net.classifier() def denseNet(net): # type: (layer.net) -> None print("Building fully connected pyramid") net.reshape(shape) # Reshape input picture net.fullDenseNet() net.classifier() # auto classes from labels # width=64 # for pcm baby data # batch=speech_data.spectro_batch_generator(1000,target=speech_data.Target.digits) # classes=10 # CHOSE MODEL ARCHITECTURE HERE: # net=layer.net(simple_dense, data=batch,input_shape=[height,width],output_width=classes, learning_rate=learning_rate) # net=layer.net(model=alex,input_width= width*height,output_width=classes, learning_rate=learning_rate) # net=layer.net(model=denseConv,input_width= width*height,output_width=classes, learning_rate=learning_rate) net = layer.net(recurrent, data=batch, input_shape=[height, width], output_width=classes, learning_rate=learning_rate) # net.train(data=batch,batch_size=10,steps=500,dropout=0.6,display_step=1,test_step=1) # debug net.train(data=batch,batch_size=10,steps=training_iters,dropout=0.6,display_step=10,test_step=100) # test # net.train(data=batch,batch_size=batch_size,steps=training_iters,dropout=0.6,display_step=10,test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random
training_steps = 500000 batch_size = 10 size = text.canvas_size def denseConv(net): # type: (layer.net) -> None print("Building dense-net") net.reshape(shape=[-1, size, size, letter.color_channels]) # Reshape input picture net.buildDenseConv(nBlocks=1) """ Baseline tests to see that your model doesn't have any bugs and can learn small test sites without efforts """ # net = layer.net(layer.baseline, input_width=size, output_width=nClasses, learning_rate=learning_rate) # net.train(data=data, test_step=1000) # run """ here comes the real network """ net = layer.net(denseConv, input_width=size, output_width=2, learning_rate=learning_rate) # net.train(data=data,steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(data=data, steps=training_steps,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, dropout=.6, display_step=5, test_step=100) # run resume # net.predict() # nil=random # net.generate(3) # nil=random
import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from letter import letter as l size = letter.max_size # def denseConv(net): # # type: (layer.net) -> None # print("Building dense-net") # net.reshape(shape=[-1, size, size, 1]) # Reshape input picture # net.buildDenseConv(nBlocks=1) # net.classifier() # 10 classes auto # # net=layer.net(alex,input_width=28, output_width=nClasses, learning_rate=learning_rate) # NOPE!? # net = layer.net(denseConv, input_width=size, output_width=letter.nClasses) # # LOAD MODEL! net = layer.net(model="denseConv", input_shape=[size, size]) # net = layer.net(model="denseConv", input_shape=[784]) # net.predict() # random : debug # net.generate(3) # nil=random def norm(mat): mat = 1 - 2 * mat / 255. # norm [-1,1] ! # mat = 1 - mat / 255. # norm [0,1]! # mat = mat / 255. # norm [0,1]! def predict(mat, norm=False):
net.classifier() # auto classes from labels train_digits=True if train_digits: width= height=64 # for pcm baby data batch=speech_data.spectro_batch_generator(1000,target=speech_data.Target.digits) classes=10 # digits else: width=512 # for spoken_words overkill data classes=74 # batch=word_batch=speech_data.spectro_batch_generator(10, width, source_data=Source.WORD_SPECTROS, target=Target.first_letter) raise Exception("TODO") X,Y=next(batch) # CHOOSE MODEL ARCHITECTURE HERE: # net = layer.net(simple_dense, data=batch, input_width=width, output_width=classes, learning_rate=0.01) net = layer.net(simple_dense, data=batch, input_shape=(width,height), output_width=classes, learning_rate=0.01) # net=layer.net(model=alex,input_shape=(width, height),output_width=10, learning_rate=learning_rate) # net=layer.net(model=denseConv, input_shape=(width, height),output_width=10, learning_rate=learning_rate) net.train(data=batch,batch_size=10,steps=500,dropout=0.6,display_step=1,test_step=1) # debug # net.train(data=batch,batch_size=10,steps=5000,dropout=0.6,display_step=5,test_step=20) # test # net.train(data=batch,batch_size=10,steps=5000,dropout=0.6,display_step=10,test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random print ("Now try switching between model architectures in line 68-71")
net.classifier() # 10 classes auto # OK, not bad, Alex! # Step 6490 Loss= 0.000908 Accuracy= 1.000 Test Accuracy: 0.995 def alex(net): # type: (layer.net) -> None print("Building Alex-net") net.reshape(shape=[-1, 28, 28, 1]) # Reshape input picture # net.batchnorm() net.conv([3, 3, 1, 64]) net.conv([3, 3, 64, 128]) net.conv([3, 3, 128, 256]) net.dense(1024, activation=tf.nn.relu) net.dense(1024, activation=tf.nn.relu) # net=layer.net(baseline, data, learning_rate=0.001) # net=layer.net(alex,data, learning_rate=0.001) # NOPE!? net = layer.net(denseConv, data, learning_rate) # net.train(steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(steps=50000,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, steps=training_iters, dropout=.6, display_step=10, test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random
# OK, not bad, Alex! # Step 6490 Loss= 0.000908 Accuracy= 1.000 Test Accuracy: 0.995 def alex(net): # type: (layer.net) -> None print("Building Alex-net") net.reshape(shape=[-1, 28, 28, 1]) # Reshape input picture # net.batchnorm() net.conv([3, 3, 1, 64]) net.conv([3, 3, 64, 128]) net.conv([3, 3, 128, 256]) net.dense(1024, activation=tf.nn.relu) net.dense(1024, activation=tf.nn.relu) net = layer.net(baseline, input_width=28, output_width=nClasses, learning_rate=learning_rate) # net=layer.net(alex,input_width=28, output_width=nClasses, learning_rate=learning_rate) # NOPE!? # net=layer.net(denseConv, input_width=28, output_width=nClasses,learning_rate=learning_rate) # net.train(steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(steps=50000,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, steps=training_iters, dropout=.6, display_step=10, test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random
#!/usr/bin/python import layer import letter size = letter.max_size def denseConv(net): # type: (layer.net) -> None print("Building dense-net") net.reshape(shape=[-1, size, size, 1]) # Reshape input picture net.buildDenseConv(nBlocks=1) net.classifier() # 10 classes auto # net=layer.net(alex,input_width=28, output_width=nClasses, learning_rate=learning_rate) # NOPE!? net = layer.net(denseConv, input_width=size, output_width=letter.nClasses) net.predict() # nil=random # net.generate(3) # nil=random
label.append(tr_labels[now_i]) now_i += 1 if now_i == length: now_i = 0 yield data, label data = [] label = [] #-------------------------------------------------------------------------------------------------------------------------------------# CHOOSE MODEL ARCHITECTURE HERE: # net = layer.net(simple_dense, data=batch, input_width=width, output_width=classes, learning_rate=0.01) # net = layer.net(simple_dense, input_shape=(width,height), output_width=classes, learning_rate=0.01) # net=layer.net(model=alex,input_shape=(width, height),output_width=10, learning_rate=learning_rate) net = layer.net(model=denseConv, input_shape=(width, height), output_width=2, learning_rate=learning_rate) print net #net.train_ichikawa_2(data=ichikawa,batch_size=10,steps=20000,dropout=0.6,display_step=10,test_step=100,ckpt_name="20170904.ckpt",start_ckpt="20170817.ckpt") # debug ichikawa = test_now() #test net.accuracy_test(data=ichikawa, batch_size=10, steps=100, dropout=0.6, display_step=1, test_step=1, ckpt_name="20170823.ckpt")
def alex(net): # type: (layer.net) -> None print("Building Alex-net") net.reshape(shape=[-1, 28, 28, 1]) # Reshape input picture # net.batchnorm() net.conv([3, 3, 1, 64]) net.conv([3, 3, 64, 128]) net.conv([3, 3, 128, 256]) net.dense(1024, activation=tf.nn.relu) net.dense(1024, activation=tf.nn.relu) net = layer.net(baseline, input_shape=[28, 28], output_width=nClasses, learning_rate=0.001) # net=layer.net(alex,data, learning_rate=0.001) # NOPE!? # net=layer.net(denseConv, data=data, output_width=-1,learning_rate=learning_rate) # net.train(steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(steps=50000,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, steps=training_iters, dropout=.6, display_step=10, test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random
# net.buildDenseConv(nBlocks=1) net.conv2d(20) net.argmax2d() net.regression(dimensions=2) # for def denseConv(net): # type: (layer.net) -> None print("Building dense-net") net.reshape(shape=[-1, size, size, letter.color_channels]) # Reshape input picture net.buildDenseConv(nBlocks=1) """ Baseline tests to see that your model doesn't have any bugs and can learn small test sites without efforts """ # net = layer.net(layer.baseline, input_width=size, output_width=nClasses, learning_rate=learning_rate) # net.train(data=data, test_step=1000) # run """ here comes the real network """ # net = layer.net(denseConv, input_width=size, output_width=2, learning_rate=learning_rate) net = layer.net(positionGanglion, input_width=size, output_width=output_shape, learning_rate=learning_rate) # net.train(data=data,steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(data=data, steps=training_steps,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, dropout=.6, display_step=5, test_step=100) # run resume # net.predict() # nil=random # net.generate(3) # nil=random
print("Building dense-net") # tf.image.crop_and_resize() net.reshape(shape=[-1, size, size, 1]) # Reshape input picture # net.conv([3, 3, 1, 64]) net.buildDenseConv(nBlocks=1) # net.dense(96*3) net.classifier() # 10 classes auto def alex(net): # type: (layer.net) -> None print("Building Alex-net") net.reshape(shape=[-1, size, size, 1]) # Reshape input picture # net.batchnorm() net.conv([3, 3, 1, 64]) net.conv([3, 3, 64, 128]) net.conv([3, 3, 128, 256]) net.dense(1024,activation=tf.nn.relu) net.dense(1024,activation=tf.nn.relu) # net=layer.net(baseline, input_shape=[28,28], output_width=nClasses,learning_rate=0.001) # net=layer.net(alex,data, learning_rate=0.001) # NOPE!? net=layer.net(denseConv, input_shape=[size, size], output_width=nClasses,learning_rate=learning_rate) # net.train(steps=50000,dropout=0.6,display_step=1,test_step=1) # debug # net.train(steps=50000,dropout=0.6,display_step=5,test_step=20) # test net.train(data=data, steps=training_iters, dropout=.6, display_step=10, test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random
# width=64 # for pcm baby data # batch=speech_data.spectro_batch_generator(1000,target=speech_data.Target.digits) # classes=10 width = 512 # for spoken_words overkill data classes = 74 # batch = word_batch = speech_data.spectro_batch_generator( 10, width, source_data=Source.WORD_SPECTROS, target=Target.first_letter) X, Y = next(batch) # CHOSE MODEL ARCHITECTURE HERE: # net=layer.net(simple_dense, width*width, classes, learning_rate=0.01) # net=layer.net(model=alex,input_width=64*64,output_width=10, learning_rate=0.001) net = layer.net(model=denseConv, input_width=64 * 64, output_width=10, learning_rate=0.001) net.train(data=batch, batch_size=10, steps=500, dropout=0.6, display_step=1, test_step=1) # debug # net.train(data=batch,batch_size=10,steps=5000,dropout=0.6,display_step=5,test_step=20) # test # net.train(data=batch,batch_size=10,steps=5000,dropout=0.6,display_step=10,test_step=100) # run # net.predict() # nil=random # net.generate(3) # nil=random