I have created dummy data for an autoencoder model using tf.data.dataset. I am using tensorflow-1.15.x, keras 2.3.1 and numpy 1.19.5 for running on my GPU.
N = 5000
H = 128
W = 128
C = 2
train_data = np.random.randn(N,H,W,C)
train_data = tf.convert_to_tensor(train_data)
test_data = np.random.randn(500,H,W,C)
test_data = tf.convert_to_tensor(test_data)
batch_size = 1
train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_data))
train_dataset = train_dataset.batch(batch_size, drop_remainder=True)
test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_data))
test_dataset = test_dataset.batch(batch_size, drop_remainder=True)
But while fitting this data on the model, I get the following error -
epochs = 5
rms = RMSprop(learning_rate=0.00002,
rho=0.9,
epsilon=1e-07)
model.compile(loss='mean_squared_error', optimizer=rms, metrics=['mean_squared_error'])
callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
logs = "logs/"
tboard_callback = tf.keras.callbacks.TensorBoard(log_dir = logs,
histogram_freq = 1)
history = model.fit(train_dataset, epochs=epochs, batch_size=batch_size, callbacks=[callback, tboard_callback],
validation_data=test_dataset)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-47134a802115> in <module>()
15
16 history = model.fit(train_dataset, epochs=epochs, batch_size=batch_size, callbacks=[callback, tboard_callback],
---> 17 validation_data=test_dataset)
2 frames
/tensorflow-1.15.2/python3.7/tensorflow_core/python/keras/engine/training.py in _validate_or_infer_batch_size(self, batch_size, steps, x)
1813 'The `batch_size` argument must not be specified for the given '
1814 'input type. Received input: {}, batch_size: {}'.format(
-> 1815 x, batch_size))
1816 return
1817
ValueError: The `batch_size` argument must not be specified for the given input type. Received input: <DatasetV1Adapter shapes: ((1, 128, 128, 2), (1, 128, 128, 2)), types: (tf.float64, tf.float64)>, batch_size: 1
Answers to other questions on this error suggest batching both train and validation datasets, but I have batched both already. What could be causing this error? Thanks.
Remove the batch_size in model.fit()
history = model.fit(train_dataset, epochs=epochs, callbacks=[callback, tboard_callback],validation_data=test_dataset)
Related
I am trying to build a machine learning model using pre-trained VGG16 with tensorflow, but I keep getting the same problem with the shape of the input. Compared to other public codes, the only difference is that I use a tf.data.dataset to share the data, instead of the DirectoryIterator of tf.image
Here is my code:
zip_ref = ZipFile(zip_file, 'r')
zip_ref.extractall(repository_dir)
zip_ref.close()
train_dir = os.path.join(repository_dir, "seg_train", "seg_train")
test_dir = os.path.join(repository_dir, "seg_test", "seg_test")
os.system(f"rm -r {os.path.join(repository_dir, 'seg_pred')}")
# load variables
validation_percentage = 0.2
label_mode = "int"
# for our model purposes
img_size = (224, 224)
color_mode='rgb'
data_train, data_val = image_dataset_from_directory(
train_dir,
batch_size=None,
label_mode=label_mode,
color_mode=color_mode,
image_size=img_size,
validation_split=validation_percentage,
subset="both",
seed=123,
)
data_test = image_dataset_from_directory(
test_dir,
batch_size=None,
label_mode=label_mode,
color_mode=color_mode,
image_size=img_size,
)
classes = data_train.class_names
print(classes)
scale = 1.0/255
normalization_layer = tf.keras.layers.Rescaling(scale)
data_train_norm = data_train.map(lambda x,y: (normalization_layer(x), y))
data_val_norm = data_val.map(lambda x,y: (normalization_layer(x), y))
data_test_norm = data_test.map(lambda x,y: (normalization_layer(x), y))
input_size = None
for img, label in data_train_norm.take(1).as_numpy_iterator():
input_size = img.shape
print(input_size)
base_model = VGG16(
input_shape=input_size, # Shape of our images
include_top = False, # Leave out the last fully connected layer
weights = 'imagenet'
)
# we do not train the parameters
for layer in base_model.layers:
layer.trainable = False
# Flatten the output layer to 1 dimension
x = layers.Flatten()(base_model.output)
# https://medium.com/analytics-vidhya/car-brand-classification-using-vgg16-transfer-learning-f219a0f09765
# FC layer very simple and with a softmax activation unit
x = layers.Dense(len(classes), activation="softmax")(x)
landscapeModel01 = Model(inputs=base_model.input, outputs=x, name="landscapeModel01")
loss = "sparse_categorical_crossentropy"
optimizer = "adam"
landscapeModel01.compile(
optimizer=optimizer,
loss=loss,
metrics=["loss","accuracy"]
)
#fit data
shuffle=True # variable
epochs=50 # variable, according if it is able to converge
batch_size = 200
print(landscapeModel01.input)
landscapeModel01.fit(
data_train_norm,
validation_data=data_val_norm,
epochs=epochs,
shuffle=shuffle,
batch_size=batch_size
)
and this is the error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [10], line 8
4 batch_size = 200
6 print(landscapeModel01.input)
----> 8 landscapeModel01.fit(
9 data_train_norm,
10 validation_data=data_val_norm,
11 epochs=epochs,
12 shuffle=shuffle,
13 batch_size=batch_size
14 )
File ~/anaconda3/envs/faa/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File /tmp/__autograph_generated_file8y_bf523.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
ValueError: in user code:
File "/home/renan/anaconda3/envs/faa/lib/python3.10/site-packages/keras/engine/training.py", line 1160, in train_function *
return step_function(self, iterator)
File "/home/renan/anaconda3/envs/faa/lib/python3.10/site-packages/keras/engine/training.py", line 1146, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/home/renan/anaconda3/envs/faa/lib/python3.10/site-packages/keras/engine/training.py", line 1135, in run_step **
outputs = model.train_step(data)
File "/home/renan/anaconda3/envs/faa/lib/python3.10/site-packages/keras/engine/training.py", line 993, in train_step
y_pred = self(x, training=True)
File "/home/renan/anaconda3/envs/faa/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/renan/anaconda3/envs/faa/lib/python3.10/site-packages/keras/engine/input_spec.py", line 295, in assert_input_compatibility
raise ValueError(
ValueError: Input 0 of layer "landscapeModel01" is incompatible with the layer: expected shape=(None, 224, 224, 3), found shape=(224, 224, 3)
What can I fix to make the code work?
versions:
tensorflow==2.10.0
#EDIT
I just found the solution: I was loading images with a batch size equals none, but the trained model demanded that the images had one, even if it was 1.
Solution
I just needed to load images in the image_dataset_from_directory with a batch_size parameter different from None. Considering my investigation did not consider data augmentation in the beginning, I chose 1.
I have tabular data with two columns , one is input feature and anther one is target variable.
I have converted the input feature to embeddings and pass on to Keras model layer, getting error while executing fit method.
here is the code which i have used for this.
df is the input dataframe and contains two columns.
X = df.drop(columns = ['data_type']).copy()
y = df['data_type']
X_train, X_rem, y_train, y_rem = train_test_split(X,y, train_size=0.8)
X_valid, X_test, y_valid, y_test = train_test_split(X_rem,y_rem, test_size=0.5)
Y_train=pd.DataFrame({'data_type':y_train})
Y_valid=pd.DataFrame({'data_type':y_valid})
Y_test=pd.DataFrame({'data_type':y_test})
enc = OrdinalEncoder()
X_train[["data"]] = enc.fit_transform(X_train[["data"]])
X_valid[["data"]] = enc.fit_transform(X_valid[["data"]])
X_test[["data"]] = enc.fit_transform(X_test[["data"]])
X_train_actual = []
X_train_actual.append(np.array(X_train["data"]).reshape(-1,1))
X_valid_actual = []
X_valid_actual.append(np.array(X_valid["data"]).reshape(-1,1))
Y_train["data_type"] = LabelEncoder().fit_transform(Y_train["data_type"])
Y_valid["data_type"] = LabelEncoder().fit_transform(Y_valid["data_type"])
Y_test["data_type"] = LabelEncoder().fit_transform(Y_test["data_type"])
Y_train_actual= to_categorical(Y_train.to_numpy())
Y_valid_actual= to_categorical(Y_valid.to_numpy())
embedding_size = 50
categories = 7
inputs = Input(shape=(1,))
embedding_layer = Embedding(categories,
embedding_size,
embeddings_regularizer=regularizers.l2(0.01),
input_length=1)(inputs)
cat_input_layers = Reshape(target_shape=(embedding_size,))(embedding_layer)
building the layers is as follows.
dense_1 = Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.01))(cat_input_layers)
final_out = Dense(7, activation='softmax')(dense_1)
model = keras.Model(inputs=inputs,outputs=final_out)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00001)
model.compile(loss='mse', optimizer=optimizer, metrics=['mae', 'mse'])
checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
verbose=1)
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)
history = model.fit(X_train_actual,
Y_train_actual,
batch_size=100,
epochs=500,
validation_data =(X_valid_actual, Y_valid_actual),
callbacks=[checkpoint, early_stop],
verbose=1)
getting the error when i am executing the fit call.
InvalidArgumentError Traceback (most recent call last)
Cell In [336], line 1
----> 1 history = model.fit(X_train_actual,
2 Y_train_actual,
3 batch_size=100,
4 epochs=500,
5 validation_data =(X_valid_actual, Y_valid_actual),
6 callbacks=[checkpoint, early_stop],
7 verbose=1)
File ~\Anaconda3\envs\keras-tf-tcb\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
any idea, what exactly the issue,
I am currently trying to do a classification problem using a pre-trained transformer model. I wrote a custom generator using tf.data.Dataset.from_generator method. The model takes two inputs : input_id and attn_mask. While calling model.fit i am getting value error "not enough values to unpack (expected 2, got 1)" The received arguments list shows it got both input_id and attn_mask. Can anyone help me solve this?
import tensorflow.keras as keras
from tensorflow.keras.models import Model
from transformers import TFBertModel,BertConfig
def _input_fn():
x = (train_data.iloc[:,0:512]).to_numpy()
y = (train_data.iloc[:,512:516]).to_numpy()
attn = np.asarray(np.tile(attn_mask,x.shape[0]).reshape(-1,512))
def generator():
for s1, s2, l in zip(x, attn, y):
yield {"input_id": s1, "attn_mask": s2}, l
dataset = tf.data.Dataset.from_generator(generator, output_types=({"input_id": tf.int32, "attn_mask": tf.int32}, tf.int32))
#dataset = dataset.batch(2)
#dataset = dataset.shuffle
return dataset
train_data is the dataframe containing the training data(16000 x 516). Last four columns are one hot encoded labels. Since i am not using the Autotokenizer function, i am passing the attention mask as attn_mask.
my model
bert = 'bert-base-uncased'
config = BertConfig(dropout=0.2, attention_dropout=0.2)
config.output_hidden_states = False
transformer_model = TFBertModel.from_pretrained(bert, config = config)
input_ids_in = tf.keras.layers.Input(shape=(512), name='input_id', dtype='int32')
input_masks_in = tf.keras.layers.Input(shape=(512), name='attn_mask', dtype='int32')
embedding_layer = transformer_model(input_ids_in, attention_mask=input_masks_in)[0]
#cls_token = embedding_layer[:,0,:]
#X = tf.keras.layers.BatchNormalization()(cls_token)
X = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(50, return_sequences=True, dropout=0.1, recurrent_dropout=0.1))(embedding_layer)
X = tf.keras.layers.GlobalMaxPool1D()(X)
#X = tf.keras.layers.BatchNormalization()(X)
X = tf.keras.layers.Dense(50, activation='relu')(X)
X = tf.keras.layers.Dropout(0.2)(X)
X = tf.keras.layers.Dense(4, activation='softmax')(X)
model = tf.keras.Model(inputs=[input_ids_in, input_masks_in], outputs = X)
for layer in model.layers[:3]:
layer.trainable = False
optimizer = tf.keras.optimizers.Adam(0.001, beta_1=0.9, beta_2=0.98,
epsilon=1e-9)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['categorical_accuracy'])
epochs = 1
batch_size =2
history = model.fit(_input_fn(), epochs= epochs, batch_size= batch_size, verbose=2)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_16908/300834086.py in <module>
2 batch_size =2
3 #history = model.fit(trainDataGenerator(batch_size), epochs= epochs, validation_data=valDataGenerator(batch_size), verbose=2) #
----> 4 history = model.fit(_input_fn(), epochs= epochs, batch_size= batch_size, verbose=2) #validation_data=val_ds,
~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
~/.local/lib/python3.8/site-packages/transformers/models/bert/modeling_tf_bert.py in call(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
1124 kwargs_call=kwargs,
1125 )
-> 1126 outputs = self.bert(
1127 input_ids=inputs["input_ids"],
1128 attention_mask=inputs["attention_mask"],
~/.local/lib/python3.8/site-packages/transformers/models/bert/modeling_tf_bert.py in call(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
771 raise ValueError("You have to specify either input_ids or inputs_embeds")
772
--> 773 batch_size, seq_length = input_shape
774
775 if inputs["past_key_values"] is None:
ValueError: Exception encountered when calling layer "bert" (type TFBertMainLayer).
not enough values to unpack (expected 2, got 1)
Call arguments received:
• input_ids=tf.Tensor(shape=(512,), dtype=int32)
• attention_mask=tf.Tensor(shape=(512,), dtype=int32)
• token_type_ids=None
• position_ids=None
• head_mask=None
• inputs_embeds=None
• encoder_hidden_states=None
• encoder_attention_mask=None
• past_key_values=None
• use_cache=True
• output_attentions=False
• output_hidden_states=False
• return_dict=True
• training=True
• kwargs=<class 'inspect._empty'>
Edit:
Adding the output of calling _input_fn()
<FlatMapDataset shapes: ({input_id: <unknown>, attn_mask: <unknown>}, <unknown>), types: ({input_id: tf.int32, attn_mask: tf.int32}, tf.int32)>
I solved this error by batching my tf.data.Dataset. This gave the TensorSpec in my dataset a shape that had two values to unpack ->
TensorSpec(shape=(16, 200)...
This is what the error refers to.
Solution
print(train_ds) #Before Batching
new_train_ds = train_ds.batch(16, drop_remainder=True)
print(new_train_ds) #After Batching
# Before Batching
<MapDataset element_spec=({'input_ids': TensorSpec(shape=(200,),
dtype=tf.float64, name=None), 'attention_mask': TensorSpec(shape=
(200,), dtype=tf.float64, name=None)}, TensorSpec(shape=(11,),
dtype=tf.float64, name=None))>
# After Batching
<BatchDataset element_spec=({'input_ids': TensorSpec(shape=(16, 200),
dtype=tf.float64, name=None), 'attention_mask': TensorSpec(shape=(16,
200), dtype=tf.float64, name=None)}, TensorSpec(shape=(16, 11),
dtype=tf.float64, name=None))>
I use CRNN (CNN + RNN + CTC Loss) for my model on OCR. I'm using Tensorflow Keras
here's my code [from CTC Loss]:
labels = Input(name='the_labels', shape=[max_label_len], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
def ctc_lambda_func(args):
y_pred, labels, input_length, label_length = args
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([outputs, labels, input_length, label_length])
#model to be used at training time
model = Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = 'adam')
filepath="best_model.hdf5"
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
callbacks_list = [checkpoint]
training_img = np.array(training_img)
train_input_length = np.array(train_input_length)
train_label_length = np.array(train_label_length)
valid_img = np.array(valid_img)
valid_input_length = np.array(valid_input_length)
valid_label_length = np.array(valid_label_length)
Error here while training:
batch_size = 256
epochs = 10
model.fit(x=[training_img, train_padded_txt, train_input_length, train_label_length], y=np.zeros(len(training_img)),
batch_size=batch_size, epochs = epochs,
validation_data = ([valid_img, valid_padded_txt, valid_input_length, valid_label_length], [np.zeros(len(valid_img))]),
verbose = 1, callbacks = callbacks_list)
ERROR RESULT:
Train on 448 samples, validate on 49 samples
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-15-1322212af569> in <module>()
4 batch_size=batch_size, epochs = epochs,
5 validation_data = ([valid_img, valid_padded_txt, valid_input_length, valid_label_length], [np.zeros(len(valid_img))]),
----> 6 verbose = 1, callbacks = callbacks_list)
7 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: sequence_length(0) <= 18
[[node ctc/CTCLoss (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_12073]
Function call stack:
keras_scratch_graph
My CRNN architecture is inspired by VGG-16, I'm using 13 conv layers and 3 bi-directional LSTM Layer. I am using CTC Loss and then I got error.
My data is 1000 text-image contains 4-8 words (700 for training&valid, 300 for testing)
if you want to view my code: here's my code using google colab.
https://colab.research.google.com/drive/1nMRNUsLDNrpgeTxPFQ4mhobnFdpbmwUx
I fixed this error. It's because of this!
Before:
# split the 700 data into validation and training dataset as 10% and 90% respectively
if i%10 == 0:
valid_orig_txt.append(txt)
valid_label_length.append(len(txt))
valid_input_length.append(31)
valid_img.append(img)
valid_txt.append(encode_to_labels(txt))
else:
orig_txt.append(txt)
train_label_length.append(len(txt))
train_input_length.append(31)
training_img.append(img)
training_txt.append(encode_to_labels(txt))
After:
# split the 700 data into validation and training dataset as 10% and 90% respectively
if i%10 == 0:
valid_orig_txt.append(txt)
valid_label_length.append(len(txt))
valid_input_length.append(18)
valid_img.append(img)
valid_txt.append(encode_to_labels(txt))
else:
orig_txt.append(txt)
train_label_length.append(len(txt))
train_input_length.append(18)
training_img.append(img)
training_txt.append(encode_to_labels(txt))
Problem:
When I run the model.predict() on test_X and train_X I get a shape mismatch when I try to learn how to predict 1 step into the future using n amount TimeSteps for lookback.
What I am using:
Windows10,
Python3.8,
Keras 2.3.1,
inside conda venv
I have tried:
Created Supervised training set from Train and Test data using the examples from https://machinelearningmastery.com/convert-time-series-supervised-learning-problem-python/.
I have tried many examples from here and MachineLearningMastery and ended with this last one. https://www.pytorials.com/predict-cryptocurrency-price-using-lstm/. I think I might have read the correct answer somewhere in the last 2 days but are now so info swamped, not entirely sure what is next anymore.
# define model
model = Sequential()
model.add(LSTM(50, activation= 'relu' , input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
callback = [EarlyStopping(monitor="loss", min_delta = 0.00001, patience = 20, mode = 'auto', restore_best_weights=True)]
# fit model
history = model.fit(train_X, train_y, epochs=100, validation_data=(test_X, test_y), verbose=1,
batch_size=16, shuffle=False, callbacks=callback)
The plots show that the loss and the val_loss converge good and nothing out of the ordinary.
Here are the shapes of my data just before prediction.
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
(25607, 60, 3) (25607,) (6360, 60, 3) (6360,)
# make predictions
trainPredict = model.predict(train_X)
testPredict = model.predict(test_X)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform(train_y)
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform(test_y)
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(train_y[:,0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(test_y[:,0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-128-11d08dc2a08f> in <module>
3 testPredict = model.predict(test_X)
4 # invert predictions
----> 5 trainPredict = scaler.inverse_transform(trainPredict)
6 trainY = scaler.inverse_transform(train_y)
7 testPredict = scaler.inverse_transform(testPredict)
c:\users\user\miniconda3\lib\site-packages\sklearn\preprocessing\_data.py in inverse_transform(self, X)
434 force_all_finite="allow-nan")
435
--> 436 X -= self.min_
437 X /= self.scale_
438 return X
ValueError: non-broadcastable output operand with shape (25607,1) doesn't match the broadcast shape (25607,4)
PS: Where does the 4 come from as not one of my other shapes have it.
Thanks in advance for any help or guidance.
EDIT
Also tried reshaping the predictors as follows but with the same error:
trainPredict = scaler.inverse_transform(trainPredict.reshape(-1,1))
trainY = scaler.inverse_transform(train_y)
testPredict = scaler.inverse_transform(testPredict.reshape(-1,1))
testY = scaler.inverse_transform(test_y)