pythagoras.py

from datetime import datetime

import numpy as np
from keras import Input
import keras.backend as K
from keras.callbacks import TensorBoard
from keras.engine import Model
from keras.layers import LSTM, TimeDistributed, Dense, Reshape, Flatten, LeakyReLU, Lambda
from keras.optimizers import Adam, sgd

BATCH_SIZE = 512
TRAIN_VALIDATE_SPLIT = 0.1
EPOCHS = 50
OPTIMIZER = Adam(lr=0.01)

data = np.random.rand(50000, 2, 2)
target = np.sqrt(
    np.square(data[:, 0, 0] - data[0:, 1, 0]) +  # delta rd_x
    np.square(data[:, 0, 1] - data[0:, 1, 1]))   # delta rd_y

input = Input(name='Input', shape=(2, 2))
x = Flatten()(input)
x = Dense(16, activation='relu')(x)
x = Dense(1)(x)

model = Model(input, x)
model.compile(loss='mse', optimizer=OPTIMIZER)
model.summary()

history = model.fit(x=data,
                    y=target,
                    epochs=EPOCHS,
                    batch_size=BATCH_SIZE,
                    validation_split=TRAIN_VALIDATE_SPLIT).history

# print(history)