Tensorflow Incompatible shape error help

import tensorflow as tf import pandas as pd import numpy as np from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras import layers from tensorflow.keras.layers.experimental.preprocessing import Normalization from tensorflow.keras.layers.experimental.preprocessing import IntegerLookup from sklearn.preprocessing import LabelEncoder # Load Data data = pd.read_csv('../datasets/labeled_data/labeled_features.csv') data = data[["acousticness","danceability","energy", "instrumentalness","liveness","loudness", "valence","tempo","genre"]] # # Prepare target variable def prepare_target(dataframe, target): le = LabelEncoder() le.fit(dataframe[target]) dataframe[target] = le.transform(dataframe[target]) return dataframe data = prepare_target(data, "genre") validation_frame = data.sample(frac=0.3, random_state=1234) train_frame = data.drop(validation_frame.index) def encode_numerical_feature(feature, name, dataset): # Create Normalization layer for our feature normalizer = Normalization() # Prepare the dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the statistics of the data normalizer.adapt(feature_ds) # Normalize the input feature encoded_feature = normalizer(feature) return encoded_feature def encode_string_categorical_feature(feature, name, dataset): # Create a StringLookup layer which will turn strings into integer indices index = StringLookup() # Prepare a Dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the set of possible string values and assign them a fixed integer index index.adapt(feature_ds) # Turn the string input into integer indices encoded_feature = index(feature) # Create a CategoryEncoding for our integer indices encoder = IntegerLookup(output_mode="binary") # Prepare a dataset of indices feature_ds = feature_ds.map(index) # Learn the space of possible indices encoder.adapt(feature_ds) # Apply one-hot encoding to our indices encoded_feature = encoder(encoded_feature) return encoded_feature def encode_integer_categorical_feature(feature, name, dataset): # Create a CategoryEncoding for our integer indices encoder = IntegerLookup(output_mode="binary") # Prepare a Dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the space of possible indices encoder.adapt(feature_ds) # Apply one-hot encoding to our indices encoded_feature = encoder(feature) return encoded_feature def dataframe_to_dataset(dataframe): dataframe = dataframe.copy() labels = dataframe.pop("genre") ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels)) ds = ds.shuffle(buffer_size=len(dataframe)) return ds train_ds = dataframe_to_dataset(train_frame) val_ds = dataframe_to_dataset(validation_frame) train_ds = train_ds.batch(32) train_ds = val_ds.batch(32) #Adaptation Steps acousticness = keras.Input(shape=(1,), name="acousticness", dtype="float64") danceability = keras.Input(shape=(1,), name="danceability", dtype="float64") energy = keras.Input(shape=(1,), name="energy", dtype="float64") instrumentalness = keras.Input(shape=(1,), name="instrumentalness", dtype="float64") liveness = keras.Input(shape=(1,), name="liveness", dtype="float64") loudness = keras.Input(shape=(1,), name="loudness", dtype="float64") valence = keras.Input(shape=(1,), name="valence", dtype="float64") tempo = keras.Input(shape=(1,), name="tempo", dtype="float64") all_inputs = [ acousticness, danceability, energy, instrumentalness, liveness, loudness, valence, tempo, ] acousticness_encoded = encode_numerical_feature(acousticness, "acousticness", train_ds) danceability_encoded = encode_numerical_feature(acousticness, "danceability", train_ds) energy_encoded = encode_numerical_feature(acousticness, "energy", train_ds) instrumentalness_encoded = encode_numerical_feature(acousticness, "instrumentalness", train_ds) liveness_encoded = encode_numerical_feature(acousticness, "liveness", train_ds) loudness_encoded = encode_numerical_feature(acousticness, "loudness", train_ds) valence_encoded = encode_numerical_feature(acousticness, "valence", train_ds) tempo_encoded = encode_numerical_feature(acousticness, "tempo", train_ds) all_features = layers.concatenate( [ acousticness_encoded, danceability_encoded, energy_encoded, instrumentalness_encoded, liveness_encoded, loudness_encoded, valence_encoded, tempo_encoded, ] ) nn = layers.Dense(32, activation="relu")(all_features) nn = layers.Dropout(0.5)(nn) #nn = layers.Dense(32, activation="relu")(nn) #nn = layers.Dropout(0.5)(nn) output = layers.Dense(8,activation='softmax')(nn) model = keras.Model(all_inputs, output) model.compile("adam","categorical_crossentropy", metrics=["accuracy"]) model.fit(train_ds, epochs = 50, validation_data=val_ds) 

I have been trying to run this code, but simply cannot run it without receiving the following error:

 ValueError: Shapes (None, 1) and (None, 8) are incompatible 

Can someone help me through this?