We Keep Coding

CNN + LSTM implementation error for image classification

I am trying to implement a CNN network + LSTM to be able to predict 4 different classes based on the sequence of x-ray images, which were preprocessed to 150x150x3 shape. My X-train shape is (4067, 150, 150, 3). When I am executing the code
model.fit(), i am getting the error.
# x_train = np.reshape(x_train, (4067, 150, 150, 3))
# y_train = np.reshape(y_train, (4067, 4))
model = Sequential()
model.add(TimeDistributed(Conv2D(filters = 32,
kernel_size=(3,3),
padding='same',
activation = 'relu'),
input_shape=(None, 150, 150, 3)))
model.add(TimeDistributed(AveragePooling2D()))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(100))
model.add(Dense(24, activation='relu',name='output'))
model.add(Dense(4, activation = 'softmax'))
from tensorflow.keras.optimizers import Adam
optimizer = Adam(lr=0.001)
model.compile(optimizer = optimizer,
loss = 'categorical_crossentropy',
metrics=['accuracy'])
from tensorflow.keras.callbacks import ReduceLROnPlateau
reduce_lr = ReduceLROnPlateau(monitor = 'val_accuracy',
factor = 0.3,
patience = 2,
min_delta = 0.001,
mode = 'auto',
verbose = 1)
hist_cnn_lstm = model.fit(x_train, y_train, batch_size=64, epochs=15,
validation_data = (x_valid, y_valid),
callbacks=reduce_lr
)
ERROR:
Epoch 1/15
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-3ec61fbabcf1> in <module>()
1 hist_cnn_lstm = model.fit(x_train, y_train, batch_size=64, epochs=15,
2 validation_data = (x_valid, y_valid),
----> 3 callbacks=reduce_lr
4 )
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is
ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, None, 150, 150, 3), found shape=(None, 150, 150, 3)

Stuck implementation for CNN

def get_three_classes(x, y):
indices_0, _ = np.where(y == 0.)
indices_1, _ = np.where(y == 1.)
indices_2, _ = np.where(y == 2.)
indices = np.concatenate([indices_0, indices_1, indices_2], axis=0)
x = x[indices]
y = y[indices]
count = x.shape[0]
indices = np.random.choice(range(count), count, replace=False)
x = x[indices]
y = y[indices]
y = tf.keras.utils.to_categorical(y)
return x, y
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train, y_train = get_three_classes(x_train, y_train)
x_test, y_test = get_three_classes(x_test, y_test)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
class_names = ['aeroplane', 'car', 'bird']
def show_random_examples (x, y, p):
indices = np.random.choice(range(x.shape[0]), 10, replace = False)
x= x [indices]
y= y [indices]
p = p[indices]
plt.figure(figsize = (10,5))
for i in range(10):
plt.subplot(2,5, 1+i)
plt.imshow(x[i])
plt.xticks([])
plt.yticks([])
col = 'green' if np.argmax(y[i]) == np.argmax(p[i]) else 'red'
plt.xlabel(class_names[np.argmax(p[i])], color = col)
plt.show()
#Creating model
def create_model():
def add_conv_block(model, num_filters):
model.add(Conv2D(num_filters, 3, activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(num_filters, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.5))
return model
model = tf.keras.models.Sequential()
model.add(Input(shape=(32,32,3)))
model= add_conv_block(model, 32)
model= add_conv_block(model, 64)
model= add_conv_block(model, 128)
model.add(Flatten())
model.add(Dense(3, activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer = 'adam', metrics = ['accuracy']
)
return model
h = model.fit (
x_train/255., y_train,
validation_data = (x_test/255., y_test),
epochs=10, batch_size=128,
callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=3),
tf.keras.callbacks.ModelCheckpoint(
'/content/drive/MyDrive/models/model_val{val_accuracy:.3f}.h5',
save_best_only=True, save_weights_only=False,
monitor='val_accuracy') ]
)
model = tf.keras.models.load_model('/content/drive/MyDrive/models/model_val0.896.h5')
preds= model.predict(x_test/255.)
image = np.array(Image.open("/content/drive/MyDrive/thy3.jpg").resize((32, 32)))
print("shape: ", image.shape)
shape: (32, 32)
pr_mask = model.predict(image).round()
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1621, in predict_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1611, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1604, in run_step **
outputs = model.predict_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1572, in predict_step
return self(x, training=False)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 263, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)
Hey everyone, this is code im working on it for project but problem is i cannot implement random photos on this model.Getting this error message, I just want to get result for random photos, and model predict it.Its basic tensorflow project only using cifar10 dataset, and only 3 classes outputs. Model predict is it car, airplane or bird thats it. Why i want is lets say i found airjet photo from web and wants this model predict it, is it airplane or bird? prediction could be wrong or accurate i dont care. But problem is i cannot format photos, or maybe you can give me idea how can i deal with that . Thanks for your answers.

The image used for the predict function is in your code defined as follows:
image = np.array(Image.open("/content/drive/MyDrive/thy3.jpg").resize((32, 32)))
Which corresponds with the following shape according to the error message you posted:
(32, 32, 3)
Error message posted:
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)
But the model requires the image to have a batch dimension for the prediciton, like:
(1, 32, 32, 3)
If your image is a 3-channel with width=32 and height=32 use to reshape it:
image = np.array(Image.open("/content/drive/MyDrive/thy3.jpg").reshape((1, 32, 32, 3)))

How to fit image data correctly to a model in python?

i am trying to trained a cnn model, but i really don't understand how to do it properly. i still learning about this kind of stuff so i'm really lost. I already tried doing stuff with it but still cannot get my head around it. can someone explain to me how to do it properly. when i try to fit the train data to the model this error pops up.
WARNING:tensorflow:Model was constructed with shape (None, 224, 224, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None,).
Traceback (most recent call last):
File "G:/Skripsi/Program/training.py", line 80, in <module>
train.train()
File "G:/Skripsi/Program/training.py", line 70, in train
model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\Skripsi\Program\venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1129, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 878, in train_function *
return step_function(self, iterator)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 867, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 860, in run_step **
outputs = model.train_step(data)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 808, in train_step
y_pred = self(x, training=True)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\input_spec.py", line 227, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Exception encountered when calling layer "model" (type Functional).
Input 0 of layer "conv2d" is incompatible with the layer: expected min_ndim=4, found ndim=1. Full shape received: (None,)
Call arguments received:
• inputs=tf.Tensor(shape=(None,), dtype=int32)
• training=True
• mask=None
this is my code for training the model.
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from densenet201 import DenseNet201
import tensorflow as tf
import pandas as pd
import numpy as np
import cv2
import os
dataset_folder = "./datasets/train_datasets"
class TrainingPreprocessing:
#staticmethod
def preprocessing_train(path):
images = cv2.imread(path, 3)
images_resize = cv2.resize(src=images, dsize=(224, 224), interpolation=cv2.INTER_LINEAR)
images_normalize = cv2.normalize(images_resize, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F)
return images_normalize.reshape(224, 224, 3)
class Training:
#staticmethod
def load_data():
"""Loads and Preprocess dataset"""
train_labels_encode = []
train_labels = []
train_data = []
file_list = os.listdir(dataset_folder)
for folder in file_list:
file_list2 = os.listdir(str(dataset_folder) + '/' + str(folder))
for images in file_list2:
train_labels_encode.append(folder)
train_labels.append(folder)
train_data.append(np.array(TrainingPreprocessing.preprocessing_train(
str(dataset_folder) + '/' + str(folder) + '/' + str(images)
)))
labels = np.array(train_labels_decode)
data = np.array(train_data)
return labels, data
def split_data(self):
"""Split the preprocessed dataset to train and test data"""
x, y = self.load_data()
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(x, y, test_size=0.20, random_state=0)
print('Training data shape : ', self.x_train.shape, self.y_train.shape)
print('Testing data shape : ', self.x_test.shape, self.y_test.shape)
def train(self):
"""Compile dan fit DenseNet model"""
input_shape = 224, 224, 3
number_classes = 2
model = DenseNet201.densenet(input_shape, number_classes)
model.summary()
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=["accuracy"])
model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
model.save_weights('densenet201_best_model.h5', overwrite=True)
loss, accuracy = model.evaluate(self.x_test, self.y_test)
print("[INFO] accuracy: {:.2f}%".format(accuracy * 100))
train = Training()
train.split_data()
train.train()
and this is the code for the cnn network
from tensorflow.keras.layers import AveragePooling2D, GlobalAveragePooling2D, MaxPool2D
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Dense
from tensorflow.keras.layers import ReLU, concatenate, Dropout
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
import tensorflow.keras.backend as K
import tensorflow as tf
class DenseNet201:
def densenet(image_shape, number_classes, growth_rate=32):
def batch_relu_conv(x, growth_rate, kernel=1, strides=1):
x = BatchNormalization()(x)
x = ReLU()(x)
x = Conv2D(growth_rate, kernel, strides=strides, padding='same', kernel_initializer="he_uniform")(x)
return x
def dense_block(x, repetition):
for _ in range(repetition):
y = batch_relu_conv(x, 4 * growth_rate)
y = batch_relu_conv(y, growth_rate, 3)
x = concatenate([y, x])
return x
def transition_layer(x):
x = batch_relu_conv(x, K.int_shape(x)[-1] // 2)
x = AveragePooling2D(2, strides=2, padding='same')(x)
return x
inputs = Input(image_shape)
x = Conv2D(64, 7, strides=2, padding='same', kernel_initializer="he_uniform")(inputs)
x = MaxPool2D(3, strides=2, padding='same')(x)
for repetition in [6, 12, 48, 32]:
d = dense_block(x, repetition)
x = transition_layer(d)
x = GlobalAveragePooling2D ()(d)
output = Dense(number_classes, activation='softmax')(x)
model = Model(inputs, output)
return model

It seems you inverted data and labels (x and y) in the function:
def load_data(): which returns: return labels, data
I think you are calling model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
with label and then data. Hence the model complaining about not getting the expected data shape.

TensorFlow returning ValueError while generating simple GAN with Conv1D layer

I´m trying to setup simple GAN with TF including Conv1D layer in the discriminator model. To achieve correct output shape, I included Flatten layer.
Unfortunately, while adding generator and discriminator layer together, TF returns error "ValueError: Input tensor must be of rank 3, 4 or 5 but was 2." I tried to do the same with simplest dummy net and compiling of the GAN worked. I expect the trouble is in the input shape of the discriminator layer, but the error description does not give too much lead.
How could I deal with this type of error? Thank you in advance for your help.
def define_discriminator(n_inputs=2):
model = Sequential()
model.add(Conv1D(filters = 128, kernel_size = 2, strides=1, input_shape = (n_inputs,1) ))
model.add(LeakyReLU(alpha=0.2))
model.add(Flatten())
model.add(Dense(25, kernel_initializer='he_uniform'))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
return model
# simple dummy net
"""
model = Sequential()
model.add(Dense(25, activation='relu', kernel_initializer='he_uniform', input_dim=n_inputs))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
return model
"""
def define_generator(latent_dim, n_outputs=2):
model = Sequential()
model.add(Dense(15, activation='relu', kernel_initializer='he_uniform', input_dim=latent_dim))
model.add(Dense(n_outputs, activation='linear'))
model.summary()
return model
def define_gan(generator, discriminator):
discriminator.trainable = False
model = Sequential()
model.add(generator)
model.add(discriminator)
model.compile(loss='binary_crossentropy', optimizer='adam')
model.summary()
return model
Complete Error message here:
Traceback (most recent call last):
File "C:/pracovni_addr/python_projects/GAN_1D.py", line 181, in <module>
gan_model = define_gan(m_gen, m_disc)
File "C:/pracovni_addr/python_projects/GAN_1D.py", line 115, in define_gan
model.add(discriminator)
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\sequential.py", line 182, in add
output_tensor = layer(self.outputs[0])
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
return func(*args, **kwargs)
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 489, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\network.py", line 583, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\network.py", line 740, in run_internal_graph
layer.call(computed_tensor, **kwargs))
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\layers\convolutional.py", line 163, in call
dilation_rate=self.dilation_rate[0])
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend\tensorflow_backend.py", line 3671, in conv1d
**kwargs)
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 917, in convolution_v2
name=name)
File "C:\Users\CLIENT\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 969, in convolution_internal
"Input tensor must be of rank 3, 4 or 5 but was {}.".format(n + 2))
ValueError: Input tensor must be of rank 3, 4 or 5 but was 2.

So the error was in the wrong shape of the generator output (discriminator expects the input shape as (None, 2, 1), but only (None, 2) was given.
Problem solved with:
model.add(Reshape((n_outputs,1)))
before
model.sumary()
in define_generator block

CNN prediction using tensorflow

I'm quite new to python and tensorflow, but already managed to build, train and validate a CNN with my own database of images saved as tf.records.
Now I want the model to read in a single picture and predict in real-time. Therefore, I wanted to modify my validation script by getting rid of the parser (which decoded my images saved as tf.records) and didn't batch the input images, since I only want to predict one. Somehow I always get the following Error:
TypeError: Value passed to parameter 'input' has DataType uint8 not in list of allowed values: float16, bfloat16, float32, float64
I took a closer look at the script I used to create the tf.records and compared them to the parser I used in the scripts for training and validation, but wasn't able to find the mistake.
I would be thankful, if you could help me to find the mistake or show me an easier way to predict the classes with an already trained CNN.
import tensorflow as tf
import cv2
num_classes = 2
crop_top = 5
crop_bottom = 10
crop_sides = 5
img_size_height = 80
img_size_width = 100
model_dir = "./2cv_128fc"
def load_image():
img = cv2.imread('./dir_pred_img/img_2.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (img_size_width + (2 * crop_sides), img_size_height + crop_top + crop_bottom),
interpolation=cv2.INTER_CUBIC)
img = img[crop_top:(img_size_height + crop_top), crop_sides:(img_size_width + crop_sides)]
features = {'image': img}
return features
def conv_nn(input_layer):
conv_1 = tf.layers.conv2d(inputs=input_layer, name='conv_layer_1', filters=32, kernel_size=3, padding='same',
activation=tf.nn.relu)
pool_1 = tf.layers.max_pooling2d(inputs=conv_1, pool_size=2, strides=2)
conv_2 = tf.layers.conv2d(inputs=pool_1, name='conv_layer_2', filters=32, kernel_size=3, padding='same',
activation=tf.nn.relu)
pool_2 = tf.layers.max_pooling2d(inputs=conv_2, pool_size=2, strides=2)
flatten = tf.contrib.layers.flatten(pool_2)
fc_layer = tf.layers.dense(inputs=flatten, name='fully_connected_layer', units=128, activation=tf.nn.relu)
fc_layer = tf.layers.dropout(fc_layer, rate=0.5, noise_shape=None, seed=None)
output_layer = tf.layers.dense(inputs=fc_layer, name='output_layer', units=num_classes)
return output_layer
def model_fn(features):
input_layer = features["image"]
input_layer = tf.identity(input_layer, name="input_tensor")
input_layer = tf.reshape(input_layer, [-1, img_size_height, img_size_width, 1]) # 1.tensor 2.shape
input_layer = tf.identity(input_layer, name="input_tensor_reshaped")
logits = conv_nn(input_layer)
pred = tf.nn.softmax(logits=logits)
return pred
model = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir)
prediction = list(model.predict(input_fn=load_image))
print(prediction[0])
full error message:
WARNING:tensorflow:Input graph does not use tf.data.Dataset or contain a QueueRunner. That means predict yields forever. This is probably a mistake.
Traceback (most recent call last):
File "C:/Users/Dell/PycharmProjects/create_data/pred_img.py", line 54, in <module>
prediction = list(model.predict(input_fn=load_image))
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 577, in predict
features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1195, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "C:/Users/Dell/PycharmProjects/create_data/pred_img.py", line 47, in model_fn
logits = conv_nn(input_layer)
File "C:/Users/Dell/PycharmProjects/create_data/pred_img.py", line 27, in conv_nn
activation=tf.nn.relu)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\convolutional.py", line 417, in conv2d
return layer.apply(inputs)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 817, in apply
return self.__call__(inputs, *args, **kwargs)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\base.py", line 374, in __call__
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 757, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 194, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 868, in __call__
return self.conv_op(inp, filter)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 520, in __call__
return self.call(inp, filter)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 204, in __call__
name=self.name)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1043, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 609, in _apply_op_helper
param_name=input_name)
File "C:\Users\Dell\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 60, in _SatisfiesTypeConstraint
", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
TypeError: Value passed to parameter 'input' has DataType uint8 not in list of allowed values: float16, bfloat16, float32, float64

The following code descirbes how you can implement this with keras.
import keras
from keras.layers import Input, Convolution2D, MaxPooling2D, Cropping2D, Dense, Flatten, Dropout
from keras.preprocessing import image
from keras.models import Model
from keras.optimizers import Adam
import cv2
import numpy as np
# parameters
num_classes = 2
crop_top = 5
crop_bottom = 10
crop_sides = 5
img_size_height = 80
img_size_width = 100
channels = 3
input_shape = (img_size_height, img_size_width, channels)
activation = 'relu'
learning_rate = 0.0001
if num_classes == 2:
loss = 'binary_crossentropy'
else:
loss = 'categorical_crossentropy'
test_image = image.load_img('./data/img.png')
test_image = image.img_to_array(test_image)
input_shape = test_image.shape
def model(input_shape=input_shape):
inputs = Input(shape=input_shape)
# cropping=((pixels_from_top, pixels_from_bottom), (pixels_from_left, pixels_from_right))
cropping = Cropping2D(cropping=((crop_top, crop_bottom), (crop_sides, crop_sides)))(inputs)
conv_1 = Convolution2D(32, kernel_size=(3, 3), padding='same', activation=activation)(cropping)
pool_1 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_1)
conv_2 = Convolution2D(32, kernel_size=(3, 3), padding='same', activation=activation)(pool_1)
pool_2 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_2)
flatten = Flatten()(pool_2)
dense_1 = Dense(128, activation=activation, name='fully_connected_layer')(flatten)
dropout = Dropout(0.5)(dense_1)
outputs = Dense(num_classes, activation='softmax')(dropout)
model = Model(inputs=inputs, outputs=outputs)
adam = Adam(lr=learning_rate)
model.compile(optimizer=adam, loss=loss, metrics=['acc', 'mse', 'mae'])
model.summary()
return model
test_image = np.expand_dims(test_image, axis=0)
model = model(input_shape)
# note that without training we will only get random results
prediction = model.predict_on_batch(test_image)
print(prediction)

I found a solution.
I added following line to the end of def load_image() and returned dataset instead of feautures.
dataset = tf.data.Dataset.from_tensors(features)
return dataset