How to fit a keras model with a dataset?-CodePudding

I try to fit my keras model with a set of csv files (i dont want to load files in the memory and concat them). I tried to build a dataset with "tf.data.experimental.make_csv_dataset" (i think it works like matlab datastore?) and feed my model with "next" and "iter" but i couldnt solve the problems with input size and/or input type. I would appreciate any help. Thanks in advance.

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense


dataset = tf.data.experimental.make_csv_dataset(
    "data/Testdata/*.csv",
    batch_size=128,
    field_delim=",",
    num_epochs=1,
    select_columns=['A', 'B', 'C'],
    label_name='C')

# MLP Model
model = Sequential()
model.add(Dense(1, input_dim=5))  
model.add(Dense(5, activation='relu'))
model.add(Dense(1, activation='linear'))
model.summary()

model.compile(loss='mean_absolute_error', optimizer="adam", metrics=['mean_squared_error'])

# for batch in dataset:
X, y = next(iter(dataset))

res = model.fit(X, y, epochs=5)

CodePudding user response：

You can feed your dataset directly to model.fit with a few changes:

Create dummy data:

import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense


df = pd.DataFrame(data={'A': [50.1, 1.23, 4.5, 4.3, 3.2], 'B':[50.1, 1.23, 4.5, 4.3, 3.2], 'C':[5.2, 3.1, 2.2, 1., 3.]})

df.to_csv('data1.csv', index=False)
df.to_csv('data2.csv', index=False)

Preprocess data:

dataset = tf.data.experimental.make_csv_dataset(
    "/content/*.csv",
    batch_size=2,
    field_delim=",",
    num_epochs=1,
    select_columns=['A', 'B', 'C'],
    label_name='C')

Before processing:

for x in dataset:
  print(x)

OrderedDict([('A', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([4.5 , 1.23], dtype=float32)>), ('B', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([4.5 , 1.23], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2, 3.1], dtype=float32)>)
(OrderedDict([('A', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([50.1,  4.5], dtype=float32)>), ('B', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([50.1,  4.5], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=float32, numpy=array([5.2, 2.2], dtype=float32)>)
(OrderedDict([('A', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([ 4.3, 50.1], dtype=float32)>), ('B', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([ 4.3, 50.1], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1. , 5.2], dtype=float32)>)
(OrderedDict([('A', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.23, 4.3 ], dtype=float32)>), ('B', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.23, 4.3 ], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=float32, numpy=array([3.1, 1. ], dtype=float32)>)
(OrderedDict([('A', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([3.2, 3.2], dtype=float32)>), ('B', <tf.Tensor: shape=(2,), dtype=float32, numpy=array([3.2, 3.2], dtype=float32)>)]), <tf.Tensor: shape=(2,), dtype=float32, numpy=array([3., 3.], dtype=float32)>)

Note that the parameter shuffle of make_csv_dataset is by default set to True. That is why you might see mixed outputs.

After preprocessing the input data has 2 features from A and B:

dataset = dataset.map(lambda x, y: (tf.concat([tf.expand_dims(x['A'], axis=-1), tf.expand_dims(x['B'], axis=-1)], axis=-1), y))
for x in dataset:
  print(x)

(<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[4.5 , 4.5 ],
       [1.23, 1.23]], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2, 3.1], dtype=float32)>)
(<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[4.3, 4.3],
       [4.3, 4.3]], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>)
(<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[ 1.23,  1.23],
       [50.1 , 50.1 ]], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([3.1, 5.2], dtype=float32)>)
(<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[50.1, 50.1],
       [ 3.2,  3.2]], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([5.2, 3. ], dtype=float32)>)
(<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[4.5, 4.5],
       [3.2, 3.2]], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2, 3. ], dtype=float32)>)

Train your model:

model = Sequential()
model.add(Dense(1, input_dim=2))  
model.add(Dense(5, activation='relu'))
model.add(Dense(1, activation='linear'))
model.summary()

model.compile(loss='mean_absolute_error', optimizer="adam", metrics=['mean_squared_error'])

res = model.fit(dataset, epochs=5)

Model: "sequential_7"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_21 (Dense)            (None, 1)                 3         
                                                                 
 dense_22 (Dense)            (None, 5)                 10        
                                                                 
 dense_23 (Dense)            (None, 1)                 6         
                                                                 
=================================================================
Total params: 19
Trainable params: 19
Non-trainable params: 0
_________________________________________________________________
Epoch 1/5
5/5 [==============================] - 1s 21ms/step - loss: 10.2060 - mean_squared_error: 247.2872
Epoch 2/5
5/5 [==============================] - 0s 10ms/step - loss: 10.0791 - mean_squared_error: 241.0892
Epoch 3/5
5/5 [==============================] - 0s 8ms/step - loss: 9.9328 - mean_squared_error: 233.3316
Epoch 4/5
5/5 [==============================] - 0s 6ms/step - loss: 9.7714 - mean_squared_error: 224.4764
Epoch 5/5
5/5 [==============================] - 0s 8ms/step - loss: 9.6863 - mean_squared_error: 221.0282