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app.py

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+import gradio as gr
+import keras
+import numpy as np
+
+# All reshaping layers and their args, descriptions
+layers = {
+    "Reshape":{
+        "args":["target_shape"],
+        "descriptions":["""target_shape: Target shape. Tuple of integers, does not include the
+        samples dimension (batch size)."""]
+    },
+    "Flatten":{
+        "args":["data_format"],
+        "descriptions":["""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. 
+        channels_last corresponds to inputs with shape (batch, ..., channels) while channels_first corresponds to inputs with shape (batch, channels, ...). 
+        It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. 
+        If you never set it, then it will be "channels_last"."""]
+    },
+    "RepeatVector":{
+        "args":["n"],
+        "descriptions":["n: Integer, repetition factor."]
+    },
+    "Permute":{
+        "args":["dims"],
+        "descriptions":["""dims: Tuple of integers.
+         Permutation pattern does not include the samples dimension. Indexing starts at 1. 
+         For instance, (2, 1) permutes the first and second dimensions of the input."""]
+    },
+    "Cropping1D":{
+        "args":["cropping"],
+        "descriptions":["""cropping: Int or tuple of int (length 2) 
+        How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1). 
+        If a single int is provided, the same value will be used for both."""]
+    },
+    "Cropping2D":{
+        "args":["cropping", "data_format"],
+        "descriptions":["""cropping: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
+    If int: the same symmetric cropping is applied to height and width.
+    If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: (symmetric_height_crop, symmetric_width_crop).
+    If tuple of 2 tuples of 2 ints: interpreted as ((top_crop, bottom_crop), (left_crop, right_crop))""",
+    """data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. 
+    channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape 
+    (batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. 
+    If you never set it, then it will be "channels_last"."""],
+    },
+    "Cropping3D":{
+        "args":["cropping", "data_format"],
+        "descriptions":["""cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
+    If int: the same symmetric cropping is applied to depth, height, and width.
+    If tuple of 3 ints: interpreted as two different symmetric cropping values for depth, height, and width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop).
+    If tuple of 3 tuples of 2 ints: interpreted as ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))""",
+    """data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape
+     (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape 
+     (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. 
+     If you never set it, then it will be "channels_last"."""]
+    },
+    "UpSampling1D":{
+        "args":["size"],
+        "descriptions":["size: Integer. UpSampling factor."]
+    },
+    "UpSampling2D":{
+        "args":["size", "data_format", "interpolation"],
+        "descriptions":["size: Int, or tuple of 2 integers. The UpSampling factors for rows and columns.",
+        """data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. 
+        channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with 
+        shape (batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. 
+        If you never set it, then it will be "channels_last".""",
+        """interpolation: A string, one of "area", "bicubic", "bilinear", "gaussian", "lanczos3", "lanczos5", "mitchellcubic", "nearest"."""]
+    },
+    "UpSampling3D":{
+        "args":["size","data_format"],
+        "descriptions":["size: Int, or tuple of 3 integers. The UpSampling factors for dim1, dim2 and dim3.",
+        """data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. 
+        channels_last corresponds to inputs with shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while 
+        channels_first corresponds to inputs with shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). 
+        It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, 
+        then it will be "channels_last"."""]
+    },
+    "ZeroPadding1D":{
+        "args":["padding"],
+        "descriptions":["""padding: Int, or tuple of int (length 2), or dictionary. - If int: 
+        How many zeros to add at the beginning and end of the padding dimension (axis 1). - 
+        If tuple of int (length 2): How many zeros to add at the beginning and the end of the padding dimension ((left_pad, right_pad))."""]
+    },
+    "ZeroPadding2D":{
+        "args":["padding", "data_format"],
+        "descriptions":["""padding: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
+    If int: the same symmetric padding is applied to height and width.
+    If tuple of 2 ints: interpreted as two different symmetric padding values for height and width: (symmetric_height_pad, symmetric_width_pad).
+    If tuple of 2 tuples of 2 ints: interpreted as ((top_pad, bottom_pad), (left_pad, right_pad))""",
+    """data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. 
+    channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape 
+    (batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. 
+    If you never set it, then it will be "channels_last"."""]
+    },
+    "ZeroPadding3D":{
+        "args":["padding", "data_format"],
+        "descriptions":["""padding: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
+    If int: the same symmetric padding is applied to height and width.
+    If tuple of 3 ints: interpreted as two different symmetric padding values for height and width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad).
+    If tuple of 3 tuples of 2 ints: interpreted as ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))""",
+    """data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs
+     with shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape 
+     (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). It defaults to the image_data_format value found in your Keras config file 
+     at ~/.keras/keras.json. If you never set it, then it will be "channels_last"."""]
+    }
+}
+with gr.Blocks() as demo:
+    gr.Markdown(f'![Keras](https://res.cloudinary.com/crunchbase-production/image/upload/c_lpad,h_256,w_256,f_auto,q_auto:eco,dpr_1/x3gdrogoamvuvjemehbr)')
+    gr.Markdown("# Reshaping Layers")
+    gr.Markdown("""This app allows you to play with various Keras Reshaping layers, and is meant to be a
+    supplement to the documentation. You are free to change the layer, tensor/array shape, and arguments associated 
+    with that layer. Execution will show you the command used as well as your resulting array/tensor.
+
+    Keras documentation can be found [here](https://keras.io/api/layers/reshaping_layers/).<br>
+    App built by [Brenden Connors](https://github.com/brendenconnors).<br>
+    Built using keras==2.9.0.
+
+    <br><br><br>""")
+    
+    with gr.Row():
+        with gr.Column():
+            layers_dropdown = gr.Dropdown(choices=list(layers.keys()), value="Reshape", label="Keras Layer")
+            with gr.Box():
+                gr.Markdown("**Please enter desired shape.**")
+                desired_shape2d = gr.Dataframe(value = [[2,2]],
+                    headers = ["Rows", "Columns"], 
+                    row_count=(1, 'fixed'), 
+                    col_count=(2, "fixed"), 
+                    datatype="number",
+                    type = "numpy",
+                    interactive=True,
+                    visible = False
+                    )
+
+                desired_shape3d = gr.Dataframe(value = [[2,2,0]],
+                    headers = ["Rows", "Columns", "Depth/Channels"], 
+                    row_count=(1, 'fixed'), 
+                    col_count=(3, "fixed"), 
+                    datatype="number",
+                    type = "numpy",
+                    interactive=True,
+                    visible = True
+                    )
+
+                desired_shape4d = gr.Dataframe(value = [[2,2,2,0]],
+                    headers = ["Rows", "Columns", "Depth", "Channels"], 
+                    row_count=(1, 'fixed'), 
+                    col_count=(4, "fixed"), 
+                    datatype="number",
+                    type = "numpy",
+                    interactive=True,
+                    visible = False
+                    )
+
+                button = gr.Button("Generate Tensor")                   
+            input_arr = gr.Textbox(label = "Input Tensor",
+            interactive = False,
+            value = np.array([[1,2],[3,4]]))
+            with gr.Box():
+                gr.Markdown("**Layer Args**")
+                with gr.Row():
+                    arg1 = gr.Textbox(label='target_shape')
+                    arg2 = gr.Textbox(label='arg2',visible=False)
+                    arg3 = gr.Textbox(label='arg3',visible=False)
+                with gr.Row():
+                    desc1 = gr.Textbox(label= '', value = layers["Reshape"]["descriptions"][0])
+                    desc2 = gr.Textbox(label = '', visible=False)
+                    desc3 = gr.Textbox(label = '', visible=False)
+            result_button = gr.Button("Execute")
+        with gr.Column():
+            output = gr.Textbox(label = 'Command Used')
+            output2 = gr.Textbox(label = 'Result')
+
+    def generate_arr(layer, data1, data2, data3):
+        """
+        Create Input tensor
+        """
+        if '1D' in layer:
+            data = data1[0]
+
+        elif '2D' in layer:
+            data = data2[0]
+
+        elif '3D' in layer:
+            data = data3[0]
+
+        elif layer=="RepeatVector":
+            data = data1[0]
+
+        else:
+            data = data2[0]
+
+
+        shape = tuple([int(x) for x in data if int(x)!=0])
+        elements = [x+1 for x in range(np.prod(shape))]
+        return np.array(elements).reshape(shape)
+
+
+    def add_dim(layer):
+        """
+        Adjust dimensions component dependent on layer type
+        """
+        if '1D' in layer:
+            return gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=False)
+        elif '2D' in layer:
+            return gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False)
+        elif '3D' in layer:
+            return gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=True)
+        elif layer=="RepeatVector":
+            return gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=False)
+        return gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False)
+
+
+    def change_args(layer):
+        """
+        Change layer args dependent on layer name
+        """
+        n_args = len(layers[layer]["args"])
+        args = layers[layer]["args"]
+        descriptions = layers[layer]["descriptions"]
+        descriptions = descriptions + ['None']*3
+        args = args + ['None']*3
+        visible_bool = [True if i<=n_args else False for i in range(1,4)]
+        return gr.Textbox.update(label=args[0], visible=visible_bool[0]),\
+            gr.Textbox.update(label=args[1], visible=visible_bool[1]),\
+                gr.Textbox.update(label=args[2], visible=visible_bool[2]),\
+                gr.Textbox.update(value = descriptions[0], visible = visible_bool[0]),\
+                gr.Textbox.update(value = descriptions[1], visible = visible_bool[1]),\
+                gr.Textbox.update(value = descriptions[2], visible = visible_bool[2])
+        
+    def create_layer(layer_name, arg1, arg2, arg3):
+        """
+        Create layer given layer name and args
+        """
+        args = [arg1, arg2, arg3]
+        real_args = [x for x in args if x != '']
+        arg_str = ','.join(real_args)
+
+        return f"keras.layers.{layer_name}({arg_str})"
+
+
+    def execute(layer_name, arg1, arg2, arg3, shape1, shape2, shape3):
+        """
+        Execute keras reshaping layer given input tensor
+        """
+        args = [arg1, arg2, arg3]
+        real_args = [x for x in args if x != '']
+        arg_str = ','.join(real_args)
+        try:
+            layer = eval(f"keras.layers.{layer_name}({arg_str})")
+        except Exception as e:
+            return f"Error: {e}"
+
+        def arr(data, layer_name):
+            if layer_name == "RepeatVector":
+                shape = tuple([int(x) for x in data[0] if int(x)!=0])
+            else:
+                shape = tuple([1] + [int(x) for x in data[0] if int(x)!=0])
+            elements = [x+1 for x in range(np.prod(shape))]
+            return np.array(elements).reshape(shape)
+
+        if '1D' in layer_name:
+            inp = arr(shape1, layer_name)
+        elif '2D' in layer_name:
+            inp = arr(shape2, layer_name)
+        elif '3D' in layer_name:
+            inp = arr(shape3, layer_name)
+        elif layer_name=="RepeatVector":
+            inp = arr(shape1, layer_name)
+        else:
+            inp = arr(shape2, layer_name)
+
+        try:
+            return layer(inp)
+        except Exception as e:
+            return e
+
+    # Generate tensor
+    button.click(generate_arr, [layers_dropdown, desired_shape2d, desired_shape3d, desired_shape4d], input_arr)
+
+    # All changes dependent on layer selected
+    layers_dropdown.change(add_dim, layers_dropdown, [desired_shape2d, desired_shape3d, desired_shape4d])
+    layers_dropdown.change(change_args, layers_dropdown, [arg1, arg2, arg3, desc1, desc2, desc3])
+    layers_dropdown.change(generate_arr, [layers_dropdown, desired_shape2d, desired_shape3d, desired_shape4d], input_arr)
+    
+    # Show command used and execute it
+    result_button.click(create_layer, [layers_dropdown, arg1, arg2, arg3], output)
+    result_button.click(execute, [layers_dropdown, arg1, arg2, arg3, desired_shape2d, desired_shape3d, desired_shape4d], output2)
+
+demo.launch()

requirements.txt

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+keras==2.9.0
+numpy