TensorFlow shape incompatibole error when trying to use predict function

I have this code :
and i want to test it but I’m getting the following warning and errors

WARNING:tensorflow:Model was constructed with shape (None, 7) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7), dtype=tf.float32, name='normalization_1_input'), name='normalization_1_input', description="created by layer 'normalization_1_input'"), but it was called on an input with incompatible shape (None, 1).

and this one:

ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 7 but received input with shape (None, 1)

the first error Im getting on y = diraction_model.predict(x) line

import pandas as pd
import numpy as np

# Make NumPy printouts easier to read.
np.set_printoptions(precision=3, suppress=True)

import tensorflow as tf
from tensorflow.keras import layers

data = [[45.975, 45.81, 45.715, 45.52, 45.62, 45.65, 44.62],
        [55.67, 55.975, 55.97, 56.27, 56.23, 56.275, 56.28],
        [86.87, 86.925, 86.85, 85.78, 86.165, 86.165, 86.83],
        [64.3, 64.27, 64.285, 64.29, 64.325, 64.245, 64.31],
        [35.655, 35.735, 35.66, 35.69, 35.665, 35.63, 35.66],
        [35.655, 35.735, 35.66, 35.69, 35.665, 35.63, 35.64],
        [35.655, 35.735, 35.66, 35.69, 35.665, 35.63, 35.67],
        [35.655, 35.735, 35.66, 35.69, 35.665, 35.63, 35.645],
        [35.655, 35.735, 35.66, 35.69, 35.665, 35.63, 35.669]
        ]
lables = [0, 1, 0, 1, 1, 0, 1, 0, 1]


def do():
    d_1 = None
    for l, d in zip(lables, data):
        if d_1 is None:
            d_1 = pd.DataFrame({'close_price': [d]})
        else:
            d_1 = d_1.append({'close_price': d}, ignore_index=True)
    d_1['lable'] = lables
    dataset = d_1.copy()

    print(dataset.isna().sum())
    dataset = dataset.dropna()
    print(dataset.keys())

    train_dataset = dataset.sample(frac=0.8, random_state=0)
    test_dataset = dataset.drop(train_dataset.index)
    print(train_dataset.describe().transpose())

    train_features = train_dataset.copy()
    test_features = test_dataset.copy()

    train_labels = train_features.pop('lable')
    test_labels = test_features.pop('lable')

    print(train_dataset.describe().transpose()[['mean', 'std']])

    train_features_1 = np.array(train_features['close_price'].to_list())
    test_features_1 = np.array(test_features['close_price'].to_list())
    print(train_features_1.shape)
    print(test_features_1.shape)
    normalizer = tf.keras.layers.Normalization(axis=-1)
    ar = np.array(train_features_1)
    normalizer.adapt(ar)
    print(normalizer.mean.numpy())

    first = np.array(train_features_1[:1])

    with np.printoptions(precision=2, suppress=True):
        print('First example:', first)
        print()
        print('Normalized:', normalizer(first).numpy())

    diraction = np.array(train_features_1)

    diraction_normalizer = layers.Normalization(input_shape=[7,], axis=None)
    diraction_normalizer.adapt(diraction)

    diraction_model = tf.keras.Sequential([
        diraction_normalizer,
        layers.Dense(units=1)
    ])

    print(diraction_model.summary())

    print(diraction_model.predict(diraction[:4]))

    diraction_model.compile(
        optimizer=tf.optimizers.Adam(learning_rate=0.1),
        loss='mean_absolute_error')
    # print(train_features_1['close_price'])
    history = diraction_model.fit(
        train_features_1,
        train_labels,
        epochs=100,
        # Suppress logging.
        verbose=0,
        # Calculate validation results on 20% of the training data.
        validation_split=0.2)

    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch
    print(hist.tail())

    test_results = {}
    test_features = np.array(test_features['close_price'].to_list())
    test_results['diraction_model'] = diraction_model.evaluate(
        test_features_1,
        test_labels, verbose=0)

    x = tf.linspace(0.0, 250, 251)
    y = diraction_model.predict(x)
    print(y)

    print("end")


def main():
    do()


if __name__ == "__main__":
    main()

>Solution :

The problem is that tf.linspace is creating a 1D tensor with the shape (251,), which is not compatible with your model’s input shape (batch_size, 7). You can, for example, reshape your tensor to fit your model:

x = tf.linspace(0.0, 250, 252)
x = tf.reshape(x, (36, 7))

and it should work.