I'm making a regression model with Tensorflow, but when I use tf.keras.metrics.R2Score() as a metric, it fails with ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: <tf.Tensor: shape=(), dtype=float32, numpy=0.0> after the first epoch. (But works fine up until then) However, if I use a different metric (tf.keras.metrics.RootMeanSquaredError(), it works fine.
import pandas as pd
weather_states = pd.read_sql("SELECT stations.id, stations.capacity_kw, start, wind_speed_10m, wind_direction_10m, wind_speed_80m, wind_direction_80m, wind_speed_180m, wind_direction_180m FROM stations INNER JOIN weather_states ON stations.id = weather_states.station WHERE weather_states.source = 'openmeteo_forecast/history/best' AND stations.source = 'wind'", db_client)
grid_states = pd.read_sql("SELECT start, wind FROM grid_states", db_client)
def create_x_y(df: tuple[Any, pd.DataFrame]):
start = df[1]["start"].iloc[0]
res = df[1].sort_values("id").drop(["id", "start"], axis=1)
temp_wind = grid_states.loc[grid_states["start"] == start]["wind"].to_list()
wind_kw = temp_wind if len(temp_wind) >= 1 else None
res_flat_df = pd.DataFrame(res.to_numpy().reshape((1, -1)))
res_flat_df["wind_kw"] = wind_kw
return res_flat_df
data = pd.concat(map(create_x_y, weather_states.groupby("start"))).dropna()
from sklearn.model_selection import train_test_split
data = data.astype("float32")
train, test = train, test = train_test_split(data.dropna(), test_size=0.2)
train_y = train.pop("wind_kw")
train_x = train
test_y = test.pop("wind_kw")
test_x = test
norm = tf.keras.layers.Normalization()
norm.adapt(train_x)
model = tf.keras.Sequential([
norm,
tf.keras.layers.Dense(16, activation="linear"),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(1, activation="linear"),
])
model.compile(
optimizer=tf.keras.optimizers.legacy.Adam(0.001),
metrics=[tf.keras.metrics.R2Score(dtype=tf.float32)],
loss=tf.keras.losses.MeanSquaredError(),
)
model.fit(train_x, train_y, epochs=7, batch_size=2)
tf.keras.models.save_model(model, 'wind.keras')
print(data.describe())
0 1 2 3 4 ... 241 242 243 244 wind_kw
count 1896.0 1896.000000 1896.000000 1896.000000 1896.000000 ... 1896.000000 1896.000000 1896.000000 1896.000000 1896.000000
mean 144000.0 4.315717 189.610759 5.791377 193.830169 ... 3.881292 145.420359 4.572205 143.642405 1292.576958
std 0.0 2.482439 113.178764 2.926497 113.685887 ... 2.612259 93.293471 2.775681 94.721086 611.333721
min 144000.0 0.100000 1.000000 0.100000 1.000000 ... 0.100000 2.000000 0.000000 1.000000 34.263000
25% 144000.0 2.110000 88.000000 3.487500 90.000000 ... 1.900000 67.000000 2.500000 63.000000 793.109500
50% 144000.0 4.110000 199.000000 5.500000 231.000000 ... 3.075000 137.000000 3.940000 135.000000 1251.590000
75% 144000.0 6.220000 291.000000 7.882500 294.000000 ... 5.502500 205.000000 6.082500 205.000000 1761.926750
max 144000.0 11.670000 360.000000 15.210000 360.000000 ... 14.460000 360.000000 16.980000 360.000000 3008.125000
print(type(data))
#<class 'pandas.core.frame.DataFrame'>
print(data.dtypes)
#0 float32
#1 float32
#2 float32
#3 float32
#4 float32
# ...
#241 float32
#242 float32
#243 float32
#244 float32
#wind_kw float32
#Length: 246, dtype: object
print(data.shape)
#(1896, 246)
I can't seem to find any information online about this error when using R2Score- any ideas as to what could be the issue?
It is a bug of TensorFlow 2.13. Use TensorFlow 2.15 or higher.