I am trying to load a pandas dataframe into a tensor Dataset. The columns are text[string] and labels[a list in string format]
A row would look something like: text: "Hi, this is me in here, ...." labels: [0, 1, 1, 0, 1, 0, 0, 0, ...]
Each text has the probability of 17 labels.
I can't find a way to load the data set into as an array, and call model.fit() I read numerous answers, trying to use the following code in df_to_dataset().
I can't figure out what I am missing in this ..
labels = labels.apply(lambda x: np.asarray(literal_eval(x))) # Cast to a list
labels = labels.apply(lambda x: [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) # Straight out list ..
# ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list).
Printing one row (from the returned data set) shows:
({'text': <tf.Tensor: shape=(), dtype=string, numpy=b'Text in here'>}, <tf.Tensor: shape=(), dtype=string, numpy=b'[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 0, 0]'>)
When I don't use any casting, model.fit sends an exception, as it can't work with a string.
UnimplementedError: Cast string to float is not supported
[[node sparse_categorical_crossentropy/Cast (defined at <ipython-input-102-71a9fbf2d907>:4) ]] [Op:__inference_train_function_1193273]
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('labels')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
return ds
train_ds = df_to_dataset(df_train, batch_size=batch_size)
val_ds = df_to_dataset(df_val, batch_size=batch_size)
test_ds = df_to_dataset(df_test, batch_size=batch_size)
def build_classifier_model():
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')
encoder_inputs = preprocessing_layer(text_input)
encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder')
outputs = encoder(encoder_inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.2)(net)
net = tf.keras.layers.Dense(17, activation='softmax', name='classifier')(net)
return tf.keras.Model(text_input, net)
classifier_model = build_classifier_model()
loss = 'sparse_categorical_crossentropy'
metrics = ["accuracy"]
classifier_model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics)
history = classifier_model.fit(x=train_ds,
validation_data=val_ds,
epochs=epochs)
Maybe try preprocessing your dataframe before using tf.data.Dataset.from_tensor_slices. Here is a simple working example:
import tensorflow as tf
import tensorflow_text as tf_text
import tensorflow_hub as hub
import pandas as pd
def build_classifier_model():
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessing_layer = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/1', name='preprocessing')
encoder_inputs = preprocessing_layer(text_input)
encoder = hub.KerasLayer('https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/2', trainable=True, name='BERT_encoder')
outputs = encoder(encoder_inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.2)(net)
net = tf.keras.layers.Dense(5, activation='softmax', name='classifier')(net)
return tf.keras.Model(text_input, net)
def remove_and_split(s):
s = s.replace('[', '')
s = s.replace(']', '')
return s.split(',')
def df_to_dataset(dataframe, shuffle=True, batch_size=2):
dataframe = dataframe.copy()
labels = tf.squeeze(tf.constant([dataframe.pop('labels')]), axis=0)
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels)).batch(
batch_size)
return ds
dummy_data = {'text': [
"Improve the physical fitness of your goldfish by getting him a bicycle",
"You are unsure whether or not to trust him but very thankful that you wore a turtle neck",
"Not all people who wander are lost",
"There is a reason that roses have thorns",
"Charles ate the french fries knowing they would be his last meal",
"He hated that he loved what she hated about hate",
], 'labels': ['[0, 1, 1, 1, 1]', '[1, 1, 1, 0, 0]', '[1, 0, 1, 0, 0]', '[1, 0, 1, 0, 0]', '[1, 1, 1, 0, 0]', '[1, 1, 1, 0, 0]']}
df = pd.DataFrame(dummy_data)
df["labels"] = df["labels"].apply(lambda x: [int(i) for i in remove_and_split(x)])
batch_size = 2
train_ds = df_to_dataset(df, batch_size=batch_size)
val_ds = df_to_dataset(df, batch_size=batch_size)
test_ds = df_to_dataset(df, batch_size=batch_size)
loss = 'categorical_crossentropy'
metrics = ["accuracy"]
classifier_model = build_classifier_model()
classifier_model.compile(optimizer='adam',
loss=loss,
metrics=metrics)
history = classifier_model.fit(x=train_ds,
validation_data=val_ds,
epochs=5)
And don't forget to include the batch size in tf.data.Dataset.from_tensor_slices when using a Bert preprocessing layer. I also changed your loss function to categorical_crossentropy, since you are working with one-hot encoded labels (can at least be inferred from your question). The sparse_categorical_crossentropy loss function expects integer labels not one-hot encoded.