Код в Python и R с keras3 отличается, я не понимаю? [закрыто] ⇐ Python

[Anonymous]

Я пытаюсь преобразовать этот код, написанный в формате Python, в формат R, но результат отличается, и я не понимаю, почему?
С Python:

Код: Выделить всё

# ---- About dataset
#
max_t        = 1000
delta_t      = 0.01
features_len = 2

sequence_len = 20
predict_len  = 5

# ---- About training
#
scale         = .2       # Percentage of dataset to be used (1=all)
train_prop    = .8       # Percentage for train (the rest being for the test)
batch_size    = 32
epochs        = 5
fit_verbosity = 1        # 0 = silent, 1 = progress bar, 2 = one line per epoch

def ladybug_init(s=122):

if s>0 : random.seed(s)
ladybug_init.params_x = [ random.gauss(0.,1.) for u in range(8)]
ladybug_init.params_y = [ random.gauss(0.,1.) for u in range(8)]

def ladybug_move(t):

[ax1, ax2, ax3, ax4, kx1, kx2, kx3, kx4] = ladybug_init.params_x
[ay1, ay2, ay3, ay4, ky1, ky2, ky3, ky4] = ladybug_init.params_y

x = ax1*sin(t*(kx1+20)) + ax2*cos(t*(kx2+10)) + ax3*sin(t*(kx3+5)) + ax4*cos(t*(kx4+5))
y = ay1*cos(t*(ky1+20)) + ay2*sin(t*(ky2+10)) + ay3*cos(t*(ky3+5)) + ay4*sin(t*(ky4+5))

return x,y

# ---- Get positions
#
ladybug_init(s=16)
x,y = 0,0
positions=[]
for t in np.arange(0., max_t, delta_t):
x,y = ladybug_move(t)
positions.append([x,y])

# ---- Build rescaled dataset
#
n = int( len(positions)*scale )
dataset = np.array(positions[:n])

k = int(len(dataset)*train_prop)
x_train = dataset[:k]
x_test  = dataset[k:]

# ---- Normalize
#
mean = x_train.mean()
std  = x_train.std()
x_train = (x_train - mean) / std
x_test  = (x_test  - mean) / std

# ---- Create sequences and labels for train and test
#
xs_train, ys_train=[],[]
all_i = np.random.permutation( len(x_train) - sequence_len - 1 )

for i in all_i:
xs_train.append( x_train[ i : i+sequence_len ] )
ys_train.append( x_train[ i+sequence_len+1 ]   )

xs_test, ys_test=[],[]
for i in range( len(x_test) - sequence_len - 1):
xs_test.append( x_test[ i : i+sequence_len ] )
ys_test.append( x_test[ i+sequence_len+1 ]   )

# ---- Convert to numpy / float16

xs_train = np.array(xs_train, dtype='float16')
ys_train = np.array(ys_train, dtype='float16')
xs_test  = np.array(xs_test,  dtype='float16')
ys_test  = np.array(ys_test,  dtype='float16')

model = keras.models.Sequential()
model.add( keras.layers.InputLayer(shape=(sequence_len, features_len)) )
model.add( keras.layers.GRU(200, return_sequences=False, activation='relu') )
model.add( keras.layers.Dense(features_len) )

model.summary()

model.compile(optimizer='rmsprop',
loss='mse',
metrics   = ['mae'] )

os.makedirs(f'{run_dir}/models',   mode=0o750, exist_ok=True)
save_dir = f'{run_dir}/models/best_model.keras'

savemodel_callback = keras.callbacks.ModelCheckpoint( filepath=save_dir, monitor='val_mae', mode='max', save_best_only=True)

history=model.fit(xs_train,ys_train,
epochs  = epochs,
verbose = fit_verbosity,
validation_data = (xs_test, ys_test),
callbacks = [savemodel_callback])

s=random.randint(0,len(x_test)-sequence_len)

sequence      = x_test[s:s+sequence_len]
sequence_true = x_test[s:s+sequence_len+1]

sequence_pred = loaded_model.predict( np.array([sequence]), verbose=fit_verbosity )

С R:

Код: Выделить всё

# ---- A propos du dataset
max_t 

Подробнее здесь: [url]https://stackoverflow.com/questions/79189356/the-code-in-python-and-r-with-keras3-are-different-i-dont-understand[/url]