ValueError: 层dense_1的输入0与层不兼容

Question

I'm using tensorflow for the first time and amusing it to classify data with 18 features into 4 classes.

The dimensions of X_train are: (14125,18).

This is my code:

dataset = tf.data.Dataset.from_tensor_slices((np.array(X_train.values, dtype=float),
                               np.array(y_train.pet_category.values, dtype=float)))
train_data = dataset.shuffle(len(X_train)).batch(32)

vdataset = tf.data.Dataset.from_tensor_slices((np.array(X_val.values, dtype=float)))
val_data = vdataset.batch(32)

tfmodel = tf.keras.Sequential([
                  tf.keras.layers.Dense(15, activation=tf.nn.relu, input_shape=(18,1)),
                  tf.keras.layers.Flatten(),
                  tf.keras.layers.Dense(10, activation=tf.nn.relu),
                  tf.keras.layers.Dense(4, activation=tf.nn.softmax)
])

tfmodel.compile(optimizer='adam',
                loss=tf.keras.losses.CategoricalCrossentropy(),
                metrics=['accuracy'])

On calling tfmodel.fit(dataset, epochs=15, validation_data=val_data) , I'm getting the following error:

ValueError: Input 0 of layer dense_1 is incompatible with the layer: expected axis -1 of input shape to have value 270 but received input with shape [18, 15]

I tried looking for similar questions but couldn't find anything that'd help. Would be really helpful to solve this issue

Edit: The issue was with the version. It went away when I used a lower version of TensorFlow (v 2.1.0).

Answer 1

You are using the dataset int fit instead of train_data . I assume you are using a DataFrame called X_train and y_train and I mimicked the same with numpy and it works now. See below.

import tensorflow as tf
import numpy as np

X_train = np.random.random((14125,18))
y_train = np.random.random((14125,1))

dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_data = dataset.shuffle(len(X_train)).batch(32)
train_data = train_data.prefetch(
        buffer_size=tf.data.experimental.AUTOTUNE)

tfmodel = tf.keras.Sequential([
                  tf.keras.layers.Dense(15, activation=tf.nn.relu, input_shape=(18,)),
                  tf.keras.layers.Flatten(),
                  tf.keras.layers.Dense(10, activation=tf.nn.relu),
                  tf.keras.layers.Dense(4, activation=tf.nn.softmax)
])

tfmodel.compile(optimizer='adam',
                loss=tf.keras.losses.CategoricalCrossentropy(),
                metrics=['accuracy'])

tfmodel.fit(train_data, epochs=5)

Note: I didn't use the val_data

Train for 442 steps
Epoch 1/5
442/442 [==============================] - 1s 2ms/step - loss: 7.8375 - accuracy: 1.4159e-04
Epoch 2/5
442/442 [==============================] - 1s 2ms/step - loss: 28.5034 - accuracy: 0.0000e+00
Epoch 3/5
442/442 [==============================] - 1s 1ms/step - loss: 17.8604 - accuracy: 0.0000e+00
Epoch 4/5
442/442 [==============================] - 1s 1ms/step - loss: 3.4244 - accuracy: 2.1239e-04
Epoch 5/5
442/442 [==============================] - 1s 2ms/step - loss: 3.2791 - accuracy: 0.0160
<tensorflow.python.keras.callbacks.History at 0x7f0d8c72d630>

Answer 2

It seems that the issue was with the version of tensorflow I was using (2.3.0) I tried with the nightly build and it gave the same error. I downgraded to v2.1.0 and it worked