Closed
Description
The following code is tested with keras-nightly.
Basically two issue.
import tensorflow as tf
import os
import numpy as np
import keras
from keras import layers
from keras import ops
keras.__version__ # 3.8.0
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu", name="dense_1")(inputs)
x = layers.Dense(64, activation="relu", name="dense_2")(x)
outputs = layers.Dense(10, activation="softmax", name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Preprocess the data (these are NumPy arrays)
x_train = x_train.reshape(60000, 784).astype("float32") / 255
x_test = x_test.reshape(10000, 784).astype("float32") / 255
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]
model.compile(
optimizer=keras.optimizers.RMSprop(), # Optimizer
# Loss function to minimize
loss=keras.losses.SparseCategoricalCrossentropy(),
# List of metrics to monitor
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
print("Fit model on training data")
csv_logger = keras.callbacks.CSVLogger('training.csv')
history = model.fit(
x_train,
y_train,
batch_size=64,
epochs=2,
callbacks=[csv_logger]
)
import pandas as pd
history = pd.read_csv('training.csv')
history.head()
- The recorded scores for training data in csv file are also wrong.
Fit model on training data
Epoch 1/2
782/782 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step - loss: 0.5810 - sparse_categorical_accuracy: 0.8389
Epoch 2/2
782/782 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.1661 - sparse_categorical_accuracy: 0.9494
Training log says, for first epoch acc: 0.83 but in csv, it is .90. Also with the loss scores.