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tryconsole.py
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tryconsole.py
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import tkinter as tk
import sys
from convolutional_mlp import evaluate_lenet5
from logistic_sgd import get_image_size, get_amount_of_classes
class Display(tk.Frame):
def __init__(self):
tk.Frame.__init__(self)
self.doIt = tk.Button(self,text="Start", command=self.start, background = 'black', fg='white')
self.doIt.pack()
self.output = tk.Text(self, width=100, height=15, background = 'black', fg='white')
self.output.pack(side=tk.LEFT)
sys.stdout = self
self.scrollbar = tk.Scrollbar(self, orient="vertical", command = self.output.yview)
self.scrollbar.pack(side=tk.RIGHT, fill="y")
self.output['yscrollcommand'] = self.scrollbar.set
self.count = 1
self.configure(background='black')
self.pack()
def start(self):
# testing()
dataset = self.dataset
print (dataset)
image_x, image_y = get_image_size(dataset)
amount_classes = get_amount_of_classes(dataset)
# Pooling size
poolsize_x = self.pooling_x
poolsize_y = self.pooling_y
# Learning rate
# Epochs to be trained and batch size
user_learning_rate = self.learn_r
user_nepochs = self.epochs
user_batch = self.batch
# Size of the convolution filter windows
user_filter_x = self.filter_x
user_filter_y = self.filter_y
# Treshhold for model training
user_treshhold = 0.995
#amount of layers given by user
n=self.layers
print ('Please, wait until all iterations are completed.')
evaluate_lenet5(learning_rate=self.learn_r, n_epochs=self.epochs,
dataset=self.dataset,
nkerns=[20, 50], batch_size=self.batch)
def write(self, txt):
self.output.insert(tk.END,str(txt))
self.update_idletasks()