Create img_recog

img_recog

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+from keras.preprocessing import image
+import numpy as np
+import tensorflow as tf
+from keras.preprocessing.image import ImageDataGenerator
+# print(tf.__version__)
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    shear_range=0.4,
+    zoom_range=0.3,
+    horizontal_flip=True)
+
+training_set = train_datagen.flow_from_directory(
+    r"C:\Users\devan_jmbn6cf\Downloads\archive (3)\train",
+    target_size=(64, 64),
+    batch_size=16,
+    class_mode='sparse')
+
+test_datagen = ImageDataGenerator(rescale=1./255)
+test_set = test_datagen.flow_from_directory(
+    r"C:\Users\devan_jmbn6cf\Downloads\archive (3)\test",
+    target_size=(64, 64),
+    batch_size=16,
+    class_mode='sparse')
+cnn = tf.keras.models.Sequential()  # artifical neural network
+# convolution
+cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3,
+        activation='relu', input_shape=[64, 64, 3]))
+# pooling
+cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
+# 2nd convolution layer
+cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu'))
+cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
+# flattening
+cnn.add(tf.keras.layers.Flatten())
+# full connection
+cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
+# output layer
+cnn.add(tf.keras.layers.Dense(units=23, activation='softmax'))
+
+# training the cnn
+cnn.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
+            metrics=['accuracy'])
+
+
+cnn.fit(x=training_set, validation_data=test_set, epochs=25)
+
+
+test_image = image.load_img(
+    r'C:\Users\devan_jmbn6cf\Downloads\archive (3)\test\Eczema Photos\03DermatitisLids1.jpg', target_size=(64, 64))
+test_image = image.img_to_array(test_image)
+test_image = np.expand_dims(test_image, axis=0)
+
+result = cnn.predict(test_image)
+
+print(test_set.class_indices)
+
+predicted_class = np.argmax(result, axis=1)
+predicted_class = predicted_class
+print("ans:", predicted_class[0])
+
+# input_arr = tf.keras.utils.img_to_array(image)
+# input_arr = np.array([input_arr])  # Convert single image to a batch.
+# predictions = cnn.predict(input_arr)