Error raised in model.fit() if validation_data ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() - keras

Question

I'm trying to run a simple autoencoder model. I'm reading training data from a csv which consists of word embeddings. I have this code, but the error in the title is raised in model.fit() function and connected with my validation data. I tried many things however the error remained. I'm new in NLP and maybe my logic is totally wrong I don't know. So, I'd be appreciated if anybody can help. Here is my code:
def train_predict(df):
X_train, X_validation = train_test_split(df, test_size=0.3, random_state=42, shuffle=True)
X = X_train.iloc[:, :-1].to_numpy() #shape is (1880,220) in here
X = tf.expand_dims(X, axis=-1) #shape is (1880,220,1)
X_val = X_validation.iloc[:,:-1].to_numpy() #shape is (300,220)
X_val= tf.expand_dims(X_val, axis=-1) #shape is (300,220,1)
inputs, decoder_output, visualization = autoEncoder(X)
model = Model(inputs=inputs, outputs=decoder_output)
encoder_model = Model(inputs=inputs, outputs=visualization)
batch_size = 128
train_steps = len(X) // batch_size
val_steps = len(X_val) // batch_size
model.summary()
model.compile(optimizer='adam', metrics=['accuracy'], loss='mean_squared_error')
model.fit(X, steps_per_epoch=train_steps, validation_data=X_val, validation_steps=val_steps,epochs=100)
result = model.evaluate(X_val, steps=10)
Also the detail of my autoEncoder function code is as follows:
def autoEncoder(X_train):
inputs = tf.keras.layers.Input(shape=(X_train.shape[1],1))
# parameters
conv_1 = Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')(inputs)
max_pool_1 = MaxPool1D(pool_size=2)(conv_1)
conv_2 = Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')(max_pool_1)
max_pool_2 = MaxPool1D(pool_size=2)(conv_2)
# BOTTLE NECK
bottle_neck = Conv1D(filters=256, kernel_size=3, activation='relu', padding='same')(max_pool_2)
visualization = Conv1D(filters=1, kernel_size=3, activation='sigmoid', padding='same')(bottle_neck)
# DECODER
conv_3 = Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')(bottle_neck)
upsample_1 = UpSampling1D(size=2)(conv_3)
conv_4 = Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')(upsample_1)
upsample_2 = UpSampling1D(size=2)(conv_4)
decoder_output = Conv1D(filters=1, kernel_size=3, activation='sigmoid', padding='same')(upsample_2)
return inputs, decoder_output, visualization

Answer 1

It'd be excellent if you could copy-paste the entire stack of error that your code produces, something that everyone should follow for error-related questions because that makes debugging that much easier.
Here's an attempt to reproduce the same error using a dummy dataset:
import numpy as np
import tensorflow as tf
np.random.seed(11)
np.set_printoptions(precision=2)
def autoEncoder(X_train):
inputs = tf.keras.layers.Input(shape=(X_train.shape[1], 1))
conv_1 = tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')(inputs)
max_pool_1 = tf.keras.layers.MaxPool1D(pool_size=2)(conv_1)
conv_2 = tf.keras.layers.Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')(max_pool_1)
max_pool_2 = tf.keras.layers.MaxPool1D(pool_size=2)(conv_2)
bottle_neck = tf.keras.layers.Conv1D(filters=256, kernel_size=3, activation='relu', padding='same')(max_pool_2)
visualization = tf.keras.layers.Conv1D(filters=1, kernel_size=3, activation='sigmoid', padding='same')(bottle_neck)
conv_3 = tf.keras.layers.Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')(bottle_neck)
upsample_1 = tf.keras.layers.UpSampling1D(size=2)(conv_3)
conv_4 = tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')(upsample_1)
upsample_2 = tf.keras.layers.UpSampling1D(size=2)(conv_4)
decoder_output = tf.keras.layers.Conv1D(filters=1, kernel_size=3, activation='sigmoid', padding='same')(upsample_2)
return inputs, decoder_output, visualization
X = np.random.randn(1880, 220)
X_val = np.random.randn(300, 220)
X = np.expand_dims(X, axis=-1)
X = tf.convert_to_tensor(X) # (1880, 220, 1)
X_val = np.expand_dims(X_val, axis=-1)
X_val = tf.convert_to_tensor(X_val) # (300, 220, 1)
inputs, decoder_output, visualization = autoEncoder(X)
model = tf.keras.Model(inputs=inputs, outputs=decoder_output)
encoder_model = tf.keras.Model(inputs=inputs, outputs=visualization)
batch_size = 128
train_steps = len(X) // batch_size
val_steps = len(X_val) // batch_size
model.compile(optimizer='adam', metrics=['accuracy'], loss='mean_squared_error')
model.fit(X, steps_per_epoch=train_steps, validation_data = X_val, validation_steps=val_steps, epochs=100)
On google-colab this gives the following error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-29-a889c5a46f35> in <module>()
3 val_steps = len(X_val) // batch_size
4 model.compile(optimizer='adam', metrics=['accuracy'], loss='mean_squared_error')
----> 5 model.fit(X, steps_per_epoch=train_steps, validation_data = X_val, validation_steps=val_steps, epochs=100)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1041 (x, y, sample_weight), validation_split=validation_split))
1042
-> 1043 if validation_data:
1044 val_x, val_y, val_sample_weight = (
1045 data_adapter.unpack_x_y_sample_weight(validation_data))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in __bool__(self)
990
991 def __bool__(self):
--> 992 return bool(self._numpy())
993
994 __nonzero__ = __bool__
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
which is identical to your OP. The reason it'd be better to post the error stack is because the answer is hidden in these lines, specifically:
1043 if validation_data:
1044 val_x, val_y, val_sample_weight = (
1045 data_adapter.unpack_x_y_sample_weight(validation_data))
The format of validation_data is identical to (x, y, sample_weight). Here's what fit method documentation has to say:
validation_data will override validation_split. validation_data could be: - tuple (x_val, y_val) of Numpy arrays or tensors - tuple (x_val, y_val, val_sample_weights) of Numpy arrays - dataset For the first two cases, batch_size must be provided. For the last case, validation_steps could be provided.
I think you now understand why you're getting an error, there's no Y for the your autoencoder. Which shouldn't be of any concern since your X itself is your Y. Here's a line from an encoder tutorial that would help us in this situation:
Train the model using x_train as both the input and the target. The encoder will learn to compress the dataset from 784 dimensions to the latent space, and the decoder will learn to reconstruct the original images.
So, what you were expected to do is to write the following:
model.fit(X, X, steps_per_epoch=train_steps, validation_data=(X_val, X_val), validation_steps=val_steps, epochs=100)
which indeed starts the training!