ruslangrimov

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.33
set_session(tf.Session(config=config))

ruslangrimov

from IPython.display import HTML
import os, base64

def GetLink(fname):
    with open(fname, 'rb') as f:
        data = base64.b64encode(f.read())
        return HTML('<a href="data:application/binary;base64,{0}" download={1}>{1}</a>'.format(data.decode(), os.path.basename(fname)))

ruslangrimov

class MyCallBack(Callback):
    def __init__(self, x_v, y_v):
        super(MyCallBack, self).__init__()
        self.x_v, self.y_v = x_v, y_v
        self.ps = []
    
    def on_epoch_end(self, epoch, logs={}):
        p = self.model.predict(self.x_v).flatten()
        self.ps.append(p)
        logs['val_loss'] = log_loss(self.x_y, p)

ruslangrimov

from keras import optimizers
from keras import losses
import numpy as np

input_img_data = np.random.random((1,) + K.int_shape(model.inputs[0])[1:])

input_img = K.variable(input_img_data)
inp = Input(tensor=input_img, batch_shape=input_img_data.shape)
out = model(inp)

ruslangrimov

import tensorflow as tf
from tensorflow.python.client import timeline
from keras import backend as K

run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()

model = ...  # A Keras model

fn = K.function(model.inputs, model.outputs, options=run_options, run_metadata=run_metadata)

ruslangrimov

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from itertools import product, count


# generate uniform unit vectors
def generate_grid_vectors(n):
    'Generates matrix NxN of unit length vectors'
    v = np.random.uniform(-1, 1, (n, n, 2))